”$513 2 20m This is to certify that the thesis entitled CONCEPTUAL DATABASE MODELING FOR INTEGRATED ENERGY MANAGEMENT IN INSTITUTIONAL BUILDINGS presented by Karun Malhotra has been accepted towards fulfillment of the requirements for Agricultural Engineering _M_'S'___.degree in "' ' A ' _, Institution LIBRARY Michigan State University PLACE IN RETURN Box to remove this checkout from your record. To AVOID FINES return on or before date due. MAY BE RECALLED with earlier due date if requested. DATE DUE DATE DUE DATE DUE 8/01 cJCIFIC/DateDuepes-ms CONCEPTUAL DATABASE MODELING FOR INTEGRATED ENERGY MANAGEMENT IN INSTITUTIONAL BUILDINGS By Karun Malhotra A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Department of Agricultural Engineering 2001 ABSTRACT CONCEPTUAL DATABASE MODELING FOR INTEGRATED ENERGY MANAGEMENT IN INSTITUTIONAL BUILDINGS BY Kamn Malhotra One of the important aspects in achieving energy efficiency is identification and characterization of the information required for making energy policy and management decisions. The area of focus of this research is to develop a conceptual database model to help improve energy efficiency in institutional buildings by effective management of information and resources. The conceptual database model developed was based on the data collected by an email survey of energy administrators of Division I Research Universities. Entity Relationship (ER) diagramming method was used for the development of the model. The proof of concept for the database model was done by presentation of the model to energy administrators of two representative Division I Research Universities, and collecting data on the potential feasibility and benefits of the model as a whole and in their current university settings. Dedicated to My wife for her continues support and love iii ACKNOWLEDGEMENTS The author would like to thank Professor Timothy Mrozowski for his continued guidance, and support. The author would also like to thank Dr. Dennis Welch, and Professor David Lawrence for their continued assistance and input. iv TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES LIST OF ABBREVIATIONS CHAPTER 1 OVERVIEW AND INTRODUCTION 1.1 Introduction 1.2 Problem area 1.3 Need 1.4 Research Objectives 1.5 Research Methodology 1.6 Expected Output 1.7 Organization of the Thesis CHAPTER 2 LITERATURE REVIEW 2.1 Energy Management in Buildings 2.1.1 Holistic Energy Management 2.1.2 Structuring Energy Management and Conservation Program 2.1.3 Organizing for Energy Management 2.1.4 Integrated Energy Planning - Energy Reduction Strategies 2.1.5. Integration of Energy Cost Reduction Opportunities 2.2 Utilities and Infrastructure Management 2.2.1 Utility Infrastructure Development at a Major Research University 2.2.2 Utility Monitoring and Control System 2.2.3 Utilities Metering and Measurement 2.3 lnforrnation Management and RDBMS Integration in Facilities Management System Development 2.3.1 Facilities Inventory and Classification Manual 2.3.2 COMBINE2 (Computer Models for Building Industry in Europe) xii ACOQQ-hCA-‘d 12 12 13 14 15 16 17 19 20 21 22 24 24 25 2.3.3 Database for Facility Management Information System 26 2.4 Chapter Summary CHAPTER 3 THESIS METHODOLOGY 3.1 Overview 3.2 Data Collection Process 3.2.1 Survey Design 3.2.1.1 Campus Physical Characteristics 3.2.1.2 Energy Management, Organization and Trends 3.2.1.3 Energy Monitoring and Metering 3.2.1.4 Space Management 3.2.1.5 Building Construction Data Storage 3.2.1.6 Classroom Scheduling 3.3 Data Analysis 3.4 Conceptual Model Development 3.5 Conceptual Model Proof of Concept 3.6 Chapter Summary CHAPTER 4 DATA REPORTING AND ANALYSIS 4.1 Data Reporting 4.1.1 Campus Physical Characteristics 4.1.2 Energy Management Trends 4.1.3 Organization Structure 4.1.4 Energy Monitoring and Metering 4.1.5 Space Management 4.1.6 Building Characteristics 4.1.7 Classroom Scheduling 4.1.8 Research 4.1.9 Integrated Energy Management Database Components 4.2 Data Analysis 4.2.1 Campus Physical Characteristics, Organization Structure and Energy Management Trends 4.2.2 Energy Monitoring, Metering, and Consumption 4.2.3 Building Equipment Data 4.2.4 Space Characteristics 4.2.5 Space Scheduling Details 4.2.6 Building Construction Details 4.3 Chapter Summary vi 28 30 30 31 33 33 35 36 37 37 38 38 39 4O 4O 41 42 42 43 43 45 45 46 47 48 49 5O 53 55 CHAPTER 5 INTEGRATED ENERGY MANAGEMENT DATABASE MODEL 5.1 Integrated Energy Management Database Development Process 5.2 Entity Relationship database Modeling 5.3 ER Template to Model Identified Elements 5.3.1 Campus Physical Characteristics 5.3.2 Energy Monitoring and Metering 5.3.3 Building Equipment 5.3.4 Space Characteristics 5.3.5 Space Scheduling 5.3.6 Construction Details 5.4 Overall Conceptual Model 5.5 Logic Design 5.5.1 Table Creation 5.5.2 Standard Queries and Reports 5.6 Chapter Summary CHAPTER 6 DEVELOPING PROOF OF CONCEPT 6.1 Methodology 6.2 Case Study 1 6.2.1 Institution Overview 6.2.2 Campus Physical Characteristics 6.2.3 Energy Metering and Monitoring Process 6.2.4 Space Management Process 6.2.5 Feedback from Energy Administrator 6.3 Case Study 2 6.3.1 Institution Overview 6.3.2 Campus Physical Characteristics 6.3.3 Energy Metering and Monitoring Process 6.3.4 Space Management Process 6.3.5 Feedback from Energy Administrator 6.4 Researcher's Recommendations and Changes 6.5 Benefits from the Proposed Changes 6.6 Chapter Summary vii 56 56 59 62 63 65 66 68 68 7O 77 77 80 80 81 81 82 82 83 85 85 86 86 87 88 88 89 89 90 91 CHAPTER 7 CONCLUSIONS AND AREA OF FUTURE RESEARCH 93 7.1 Introduction 93 7.2 Conclusions 93 7.2.1 Conclusions about Energy and Organization Setup in 94 Division I Research Universities 7.2.2 Conclusions about Data Requirements in an Integrated 95 Energy Management Database 7.2.3 Conclusions about Conceptual Database Model 95 Development 7.3 Limitations of the Research 96 7.4 Area of Future Research 97 7.4.1 Energy Management Database Modeling 97 7.4.2 Integration of Facilities and Energy Management 98 7.4.3 Energy Management Software Development 98 APENDICES 99 Appendix A FICM Data Properties 100 Appendix B Survey Questions 103 Appendix C Information System Concept and Definition 112 Appendix D Information System Development 122 Appendix E Database Design Methodologies 125 Appendix F Database Design Process 128 Appendix G Database Design Strategies 132 Appendix H Data Table Properties 135 Appendix J Standard Queries 141 LIST OF REFERENCES 143 viii LIST OF TABLES Table 4.1 Integrated Energy Management Database Components 46 Table 4.2 Data Areas and their Items for Campus Physical Characteristics 48 Table 4.3 Data Areas and their Items for Energy Monitoring System 49 Table 4.4 Data Areas and their Items for Building Equipment 50 Table 4.5 Data Areas and their Items for Space Characteristics 52 Table 4.6 Data Areas and their Items for Classroom Scheduling 54 Table 4.7 Data Areas and their Items for Building Physical Characteristics 55 Table 5.1 Entities and their Attributes 73 Table 5.2 Building Data Table 78 Table 5.3 Electricity Meter Data Table 79 Table 5.4 Building Equipment Data Table 79 ix LIST OF FIGURES Figure 1.1 Integrated Energy Management Database Structure Figure 2.1 Energy Conservation Measures Figure 2.2 Hierarchical Control Pyramid and Levels of Control Figure 2.3 Conceptual Model Schema Figure 5.1 Integrated Energy Management Data Model Development Process Figure 5.2 Entity Relationship Model Example Figure 5.3 ER Model for Campus Physical Characteristics Figure 5.4 ER model for Energy Monitoring and Metering Systems Figure 5.5 ER Model for Building Equipment Figure 5.6 ER model for Building Space Characteristics. Figure 5.7 ER model for Classroom Scheduling Figure 5.8 ER model for Constmction Details Figure 5.9 Conceptual Integrated Energy Management Database Model Figure C.1.1 Data Hierarchy for a Computer Based File Figure C.1.2 Hierarchical Database Model Figure C.1.3 Network Database Model Figure 0.1.4 Relational Database Model- a Table Figure 0.1.1 The Information System Development Life Cycle Figure 6.1.1 Centralized Design 19 22 27 58 62 65 66 67 68 69 72 115 116 120 121 124 133 Figure 6.1.2 Decentralized Design 134 xi LIST OF ABBREVIATIONS APPA: The Association of Physical Plant Administrators BTU: British thermal Unit CADD: Computer Aided Design and Drawings kWh: Kilowatt-hour DBMS: Database Management System DDC: Digital Direct Control DDL: Data Definition Language DFD: Data Flow Diagram DML: Data Manipulation Language EMC: Energy Management and Conservation ER: Entity Relationship FICM: Facilities Information and Classification Manual FMIS: Facilities Management lnforrnation Systems GIS: Geographical Information System GSF: Gross Square Feet HVAC: Heating, Ventilating, and Air Conditioning IAT: Image Associated Tools IS: Information System NCES: National Center of Educational Statistics QBE: Query by Example SQL: Structured Query Language xii Chapter 1 Overview and Introduction 1.1 Introduction Higher education institutions typically serve multiple missions including instruction, research and various types of public service. In the past two decades, new and diverse roles and services have been added to these traditional missions through more specialized departments and organizations within each institution. Planning and efficient management of buildings are essential for many reasons. The amount and suitability of building space directly affect the scope and quality of educational services. Buildings are the largest component of any research university’s capital budget and require a significant portion of its annual operating expense. Inappropriate management of facilities can increase the consumption of scarce resources. The energy business is currently preoccupied with hardware efficiency retrofits. The most typical of these retrofits are retrofit of efficient motors, fans, electronic ballast and lamps. This does not require a highly trained professional for implementation. In many cases, the science of energy management and information systems is often ignored. A highly competitive hardware oriented retrofit business exists, and there are plenty of situations where an efficient heat pump, electronic ballast and/or a T8 lamp solution are appropriate. However it does take sophisticated engineering to determine the standard and its correct integration with other systems within the building (Tucker 1988). It is essential for people who understand buildings and information systems to join in integrated teams that focus on energy management for the building industry (Verderber & Siminovitch 1989). A corporate energy management program consists of the following four basic steps (Burns 1999): a) collect 8 analyze energy information, b) strategic planning, c) execute strategy and d) benchmarks results To develop a strategic energy plan, the information on energy generation and consumption, and factors effecting energy consumption should be accurate and integrated. The management of current assets and effective integration of pertinent information is the most important aspect in energy management for any research institution. The problems and solutions outlined in this thesis apply to institutional buildings in Division-I Research Universities (Carnegie Classification 1994), and in a generic form to all institutions. The overall problem is ineffective management of information and resources. A related problem is the large amount of energy being wasted by these poorly managed buildings. The US spends twice the amount that Japan or Western Europe spends on energy to produce our gross national product (Smith1990). Energy use in buildings accounts for 35% of the total energy consumption in United States and 42% of total energy costs. Cost savings for energy efficiency measures in buildings are estimated at 38 billion US dollars by 2010 (CRC handbook 1997). Higher education facilities have started to follow an integrated energy management approach to reduce their energy consumption. Integrated energy management is an approach to saving utility costs by controlling operation and maintenance practices, increasing efficiency by renewing or replacing energy using systems, and educating the university community about saving energy through procurement and use of technology. Implementing these approaches takes time. In large facility systems, integrated software can increase productivity and eliminate duplication (Nawab 1994). The Association of Physical Plant Administrators (APPA) survey of research universities indicates that although electricity consumption in research universities has increased by 5.5%, the electricity Kilo watt-hour (kWh) per building gross sq.ft has reduced by 22% from 1993 to 1998. The trend indicates that most research universities have started investing time and money in energy conservation programs, but still there is a considerable amount of savings that can be achieved if an energy management program is carefully implemented and monitored. 1.2 Problem Area Rapidly changing environmental conditions, combined with the development of new technologies, have created a need for US universities to manage their energy resources more efficiently. In the past two decades, the enrollment in Division I Research Universities has increased from 1.097 million to 2.03 million (NCES 1994), which has created a need for more instructional and research space. Between 1974 and 1989, US institutions of higher learning added approximately 1 billion gross square feet (GSF) of space to their physical plants (Rush & Johnson 1989). On the other hand, approximately one third of higher education’s physical plants are now 40 or more years old, and the remaining two thirds is 30 or more years of age. Physical plants in most research universities manage an average of 269 education and general buildings and about 116 auxiliary buildings covering approximately 8.9 million GSF of area. Academic facilities and general buildings cover about 5.7 million GSF area, which includes offices, classrooms, laboratories, and academic space in residence halls (Rush & Johnson 1989). Due to the exponential increase in the construction of new institutional buildings, energy consumption in most research universities has reached an all time high. 1.3 Need One of the important aspects in achieving energy efficiency is identifying and characterizing the information required for making energy policy and management decisions. Energy management for most campuses is largely energy management within buildings, as they are the largest energy consuming entities. A well-designed energy management plan will take into account all the factors that effect energy use in campus buildings. These factors include the size, type, and age of the buildings; the campus enrollment and nature of the academic activities; present conditions of the buildings and systems; and the personnel and financial resources required to implement and maintain increased energy efficiency projects (APPA 1994). Due to their enormous size, different organizations within the research university store data on energy generation, consumption, electricity & steam metering, equipment scheduling, energy monitoring, university space inventory, and scheduling of classes. There is a huge amount of data stored in independent customized databases that each of these organizations manages to serve their respective mission. For achieving energy efficiency in institutional buildings there is a need to integrate pertinent information from all these above-mentioned organizations to develop an integrated energy management database (Figure 1.1). Such a database would store information such as monthly electric and steam consumption, space usage characteristics, space occupancy, classroom schedules, building construction data, and mechanical and electrical systems data. An integrated database could help energy administrators in making strategic energy decisions, highlighting key areas and issues that need to be addressed for achieving energy efficiency. Bulldlng Equipment Data Building V 3;: ~ Integrated University Energy Energy Admlnlstratas Management Database Building Bulldlng Constructlon Scheduling Data data Figure 1.1 Integrated Energy Management Database Structure 1.4 Research Objectives The area of focus of this research is the development of an integrated energy management database model for institutional buildings. The goal is to achieve energy efficiency in institutional buildings through effective management of information and resources. The purpose of this model is to present data elements of a domain system in an organized and abstract representation via standard modeling. The domain system, which is being represented in a database system, is the integrated energy management system. The integrated system means that data stored include not just mechanical equipment or metering and monitoring data, but also include space related data such as space characteristics and scheduling. Based on this model a database management system (DBMS) can be built to facilitate energy information processing. The research explores database design development issues and highlights conceptual database modeling. The specific objectives of the research are: 1. To analyze the existing energy management and organization setup in representative Division-l Research Universities. 2. To identify parameters and information required to be incorporated in an integrated energy management database. 3. To develop a conceptual data model for design of an integrated energy management database system. This conceptual model represents an integrated energy management system in a database view. It can also be transferred into a logical model tailored to specific DBMS software. A database reflects an efficient way of storing, accessing, and managing end user data. A theoretical database development process is needed to produce an efficient database. Conceptual design is the most critical phase of database design (Batini, et. al, 1992). lnforrnation requirement analysis and conceptual model development is the focus of this research work. 1.5 Research Methodology The thesis is accomplished using the following steps: Development of framework model for design of an integrated energy management database. The principle components of this step include: 1. Literature review on Entity Relationship (ER) modeling techniques, database development methods, and energy management in institutional and commercial buildings, organization structure in Division-I Research Universities, and facilities management. 2. Email surveys of energy administrators in Division-I Research Universities to collect information on energy management, generation, distribution, utilization, organizational setup, physical statistics and administrative practices. 3. Identification of key attributes for an integrated energy management database by reviewing literature on energy management for institutional buildings supplemented with analysis of email surveys of Division I Research Universities. 4. Designing the conceptual model for an integrated energy management database utilizing ER modeling techniques. There are several research tasks involved in developing the model. The key attributes identified will be organized in terms of entities, relationships, and attributes. The ER modeling technique is used as a template to represent the entities and relationships that are summarized from the real world objects. The model provides a logical view of the data that meets the information requirement for an integrated energy management system. Develop a proof of concept for the proposed framework model. The principle components of this step include: 1. Developments of case study of a representative Division I Research Universities’ energy management systems. This includes presentation of the model to energy administrators of that university through structured interviews and collection of feedback on the potential feasibility and benefits of the proposed model as a whole and in their current settings. 2. Presentation of the model to an energy administrator of one representative Division-l Research University to assess its validity and feasibility in their university setting. 1.6 Expected Output The research study is intended to integrate relational database management concepts with energy management systems. The research is focused on using the ER modeling tool to develop a conceptual data model for the design of an integrated energy management database, following the systems approach of the DBMS development process. After determining the information requirements of energy administrators and information flow, data elements are organized into an Entity Relationship diagram. The conceptual model is generated using the ER modeling technique. This conceptual model is generic and is not limited to a particular database software application. Based on this model, an integrated energy management DBMS can be developed. 1.7 Organization of the Thesis The thesis is organized as follows: 1. Chapter 2 provides the research and literature review. It describes the existing research work in the field of energy management, information systems, and facilities management in commercial and institutional buildings. Chapter 3 outlines the methodology used in the thesis. It describes the data collection and data analysis method, and provides an overview of the validation procedure used in this research. Chapter 4 presents the data analysis. It reports and analyzes the data collected from the survey of Division I Research Universities. Data elements are identified and grouped based on the natural logical relationships among them. Chapter 5 focuses on the conceptual model development and explanation. ER models are developed for each subsystem and then integrated in an overall conceptual model. The entities and relationships in the conceptual model are transferred into relational database tables. Chapter 6 presents the proof of the conceptual model through structured interviews and presentation of the model to the Division I University’s 10 energy administrators. Feedback on the feasibility and benefits of the proposed model in their current university settings are examined. 6. Chapter 7 presents the conclusions, limitations, and the area of future research. 11 Chapter 2 Literature Review This chapter describes the existing research and literature in the area of energy and utilities management, and database modeling in facilities management. 2.1 Energy Management In Buildings Energy management for most higher education institutes is basically energy management of buildings. Recent research has shown that the overall potential of reducing energy use in buildings through effective management is substantial (Krieth and West 1997). Energy management research has been focused on structuring the program, organizing the energy management structure in a corporate environment, and integration of energy reduction opportunities through a structured information system. Researchers (Nawab, 1994 and Ferriera, 1999) have developed different approaches to integrated energy management. Nawab (1994), In his dissertation, analyzed 11 institutional buildings for energy reduction strategies by integrating the effects of design, systems, and management strategies on these buildings. According to Ferriera (1999), a typical integrated approach includes benchmarking each building based on construction, age, equipment, use, size, and operating hours. Energy cost reduction must be approached as an interface process combining energy audits, maintenance, 12 control programming, monitoring, and training. It cannot effectively be accomplished without the complete integration. 2.1.1 Holistic Energy Management (Kazenelberger, 1990) Saint Norbert College located in West De Pere, Wisconsin encompasses just over 800,000 gross square feet of diverse facilities on thirty-five acres. The physical plant there embarked on a broad approach of energy management that they referred to as “holistic energy management”. A successful energy management philosophy is multi—faceted, requiring solutions that respond to the specific conditions of the campus. Energy management issues, like most other management issues, are best addressed when the response to the challenge directly recognizes the environment in which it occurs. The physical plant staff at Saint Norbert College made an inventory of physical plant characteristics and conditions of existing building envelopes. They also assessed the comfort level and functionality provided by the mechanical systems, condition of campus steam and electricity distribution system, and level and condition of systems control in each of the campus buildings. The process of evaluating these components was integrated into an in-house facilities condition audit that evaluated the entire spectrum of long-range maintenance needs and resulted in identifying energy projects that could provide attractive paybacks. l3 2.1.2 Structuring an Energy Management and Conservation Program (Karkia, 1990) The California State University (CSU) system of twenty-two campuses serves approximately 365,000 students with an annual operating budget of $ 1.7 billion. In 1979, CSU started an energy management plan that resulted in reduction in energy consumption of 36% between fiscal years 1973-74 and 1986-87. The components of the plan included both enforcement of prudent energy management methods and implementation of major energy projects. Using the California State University energy and utility program as a model, Karkia developed a systematic approach for formulating and implementing a successful energy management and conservation program (EMC). The key components of Karkia’s strategic energy plan are: Organization Subsystems: For an energy management and conservation program, the organization should create a staff office, decentralize the profit centers within the campus, and create a project team or task force. It is essential to the EMC program success that top management become enthusiastically involved in the program. Securing personnel resources and determining functional placement of the personnel within the organization is an important step towards a successful EMC program. Information Subsystems: This is the information collection, organization, analysis and dissemination phase. Planning information must be current, focused towards environmental and competitive goals of the organization, and easy to access in the form in which it is most useful. Without an effective information system, 14 decisions are made without relevant information. An energy audit of the existing facilities should be preformed to collect information on existing conditions. This includes calculating energy costs and determining which buildings have a higher than normal or higher than desired energy consumption. This step also includes no cost or low cost energy conservation opportunities. Decision Subsystems: This system can be a part of an information system when the information system also acts as a decision support system, or can be an independent system. It should help in decision making and integrating the individual plans with organization goals. 2.1.3 Organizing for Energy Management (Becker and Stebbins, 1995) In IEEE Std 739-1995, Becker developed guidelines and critical factors for organizing a corporate energy management program. According to Becker, the five critical factors in organizing an effective energy management program are as follows: Obtain Top Management Energy Commitment: There should be a formally communicated, formally supported dedication to reducing energy consumption while maintaining or improving the functioning of a facility. The commitment shall be active, clear, visible, and well communicated to all levels of the organization. Obtain People Commitment: People at all levels of the organization should be involved in the program. The energy team should consist of representatives from each major facility, engineering, operations, and other departments. 15 Set up an Information System: The information system should be set up to keep updated information on results of the program, identifying high achievers. The system can also be used to advertise the program and to encourage participation. Organization Setup: The existing organization setup should give authority and commensurate responsibility for the conservation efforts and incentives for developing an energy management program. Monitoring and Controlling the Program: Establish an energy accounting and monitoring system to communicate the results and take corrective actions. 2.1.4 Integrated Energy Planning - Energy Reduction Strategies (Nawab, 1994) Nawab, 1994 in his dissertation to develop an integrated energy planning strategy for the University of Michigan analyzed and modeled 11 buildings for energy consumption using energy modeling software DOE 2.10. The effect of design, systems, and management strategies were modeled and analyzed on these buildings. The design parameters such as glazing changes, lighting energy consumption changes, system performance of multi-zone and dual duct systems. Management options like re-scheduling building operation hours and system shutdowns during weekends were also modeled. Based on his analysis the researcher suggests the following generalizations: 16 . Primary savings in institutional buildings are from direct reduction in the lighting component of peak electric load, with additional savings from reduced cooling loads. . Designs that minimize peaks will generally lower energy consumption, but exceptions are possible. Designs that minimize annual energy consumption will provide lower peaks if fenestration, lighting and HVAC systems are effectively managed. Minimizing electricity cost requires changes in both consumption patterns and peak demand. . Thermal gains through fenestration and resulting cooling loads must be effectively managed it day lighting is to provide maximum load reductions. The efficacy of day lighting/lighting systems depends greatly on fenestration properties, and window and system management. . System and plant management with known users’ schedule are the most advantageous integrated energy planning strategies in building energy reduction. 2.1.5 Integration of Energy Cost Reduction Opportunities (Ferriera, 1999) According to Ferriera (1999), a typical integrated approach includes benchmarking each building, which will define best-case scenarios for KWh/sq.ft./yr, therms/sq.ft./yr, and BTU/sq.ft./yr. Each building’s actual energy use should then be compared to benchmarks in order to determine potential energy reduction targets. If a building can be cost effectively improved to achieve the projected reductions, a detailed energy audit should be done. Models should 17 be prepared to simulate existing energy usage under existing operation patterns. A second model should be prepared that corrects disoperation, eliminates deferred maintenance, and incorporates the effects of more efficient control programming. Every aspect of building energy use should be analyzed in this . manner, from lighting to chiller plants, to develop a list of cost effective energy conservation measures (ECM). The ECM’s (Refer Figure 2.1) then should be implemented following the specific retrofits, modifications, control programming, scheduling, flow adjustment etc. that the technical audit specified. 18 ENERGY COST REDUCTION STRATEGIES MANAGEMENT/ ~ onimnhfig" OPERATIONS — : A TECHNOLOGY ”5;; STRATEGY Figure 2.1 Energy Conservation Measures Adopted from Ferriera (1999) 2.2 Utilities and Infrastructure Management Basic building utilities comprise various service distribution systems, including but not limited to electricity, heating and cooling, water and sewer, natural gas, and voice and data communications. Of all these services, electricity and heating and cooling services often play the most significant role since these utilities are more energy intensive and usually have the most impact on energy budgets. Researchers have tried to integrate energy metering and monitoring data within 19 the energy management/conservation programs. In IEEE Std 739 1995, Lennig describes the database components and function of level of control in a utility monitoring and control system. Qayoumi (1999) in his research effort presents the need to integrate utility metering with energy conservation and development of a decision support system. 2.2.1 Utility Infrastructure Development at a Major Research University (Purinton 8. Swistock, 1990) Researchers Purinton & Swistock came up with a “single system concept” for creating a utilities master plan for a research university. In this concept, the single utility system extends from the point of generation or purchase to the individual end user, such as an individual laboratory, and does not recognize the traditional separation between distribution and building systems. The capability to control functions within individual spaces permits the end user to receive the technical benefits of system scale in terms of redundancy, system inertia, and sophisticated control. According to Purinton & Swistock, the starting point is development of a utility master plan. The task is not limited to creating an inventory of existing utilities but also incorporating design for flexibility and expandability through planned multiple paths and the use of computerized controls. The benefits of the approach will be reduced maintenance and operation costs by using sophisticated control systems that can accurately and effectively match systems demand and production. Effective redundancy is 20 achieved through load diversity among users without having to physically duplicate critical equipment components. 2.2.2 Utility Monitoring and Control System (Lenning, 1995) In IEEE Std 739 1995, Lennig describes the database components and function of control in a utility monitoring system. The utility monitoring system should consider the total plant site, including fuel usage in the production of process steam, hot water, chilled water, and electricity; energy usage in the production process in maintaining the plant environment; and the cost of purchased electricity. The utility management system consists of three (3) hierarchical tiers which includes operating, supervisory, and management planning levels (Figure 2.2). The data within this hierarchy requires manipulation and refining as it progresses from a lower to higher level. Utility management systems are proposed for use in monitoring power production and purchases, which can help engineers recommend changes in the distribution or purchase of energy. Monitoring and evaluation of energy usage by department or area can prevent extraordinary energy consumption. Early detection of rising temperature, abnormal currents, or other operating irregularities during normal monitoring by the computer can signal a need for maintenance before equipment is damaged. 21 To coordinate . & predict Iong- Predicted term energy Values requirements Planning To coordinate Calculated & schedule Values hort range Su . C nergy supply pervrsory P demand 0:26? To control Alarm quuipment & Process Control Limits process Function of (Database levels of onter:ts at Control iggtro Figure 2.2 - Hierarchical Control Pyramid and Levels of Control Source IEEE Std 737-1995 2.2.3 Utilities Metering & Measurement (Qayoumi, 1999) Qayoumi (1999) in his research effort presents the need to integrate utility metering with energy conservation projects and the development of a decision support system. The researcher believes that the advent of electric utility deregulation has brought new challenges and opportunities for facilities managers of colleges, universities, and other educational campus environments. In order to prepare for a deregulated market, organizations with campuses need to spend more time and effort in Ieaming how much electricity is used, at what 22 time, and where. They must also learn whether it is possible to shift loads or change power demands without an appreciable negative impact on campus operations. The researcher also recognizes that relevant and reliable energy consumption data is required to determine load profiles, and most organizations do not have either adequate or well maintained metering. In most cases, there are branch meters that measure electricity, steam, natural gas, hot water, chilled water and auxiliary data, but these meters are not properly calibrated and sized. Many of them are electromechanical meters where data cannot be remotely read and processed for development of load profiles. The lack of good metering systems is the principal barrier that is preventing many organizations from taking full advantage of utility cost reduction opportunities. Qayoumi suggests that sub metering is the means to obtain data for trend analysis and determine consumption profiles of major load centers; that will help in energy reduction by modifying load shapes by changing the schedules of certain loads, equipment duty cycling, or installing thermal energy storage to reduce peak demand. For a successful implementation of a metering program and effective integration with the campus energy conservation program, the facilities manager should identify what elements need to be measured and design an information system that will analyze, compare, and suggest changes based on the organization’s current goals. 23 2.3 Information Management and RDBMS Integration in Facilities Management System Development Information technology has played a great role in today’s business operations. It has drawn the attention of researchers in facilities management. From facilities design modeling to database concept integration, efforts have been made to use information technology to better represent and communicate facilities information, and to achieve the goals of high quality design and effective management of existing facilities. 2.3.1 Facilities Inventory and Classification Manual (FICM, 1992) The 1992 Facilities and Classification Manual (FICM) published by the National Center of Educational Statistics (NCES) provided a common framework and coding structure to be used in collecting and reporting inventory data on college and university “buildings”, and on space within those structures, primarily “rooms”. The manual suggest to institutions a pattern of compiling essential data on their physical facilities and provides a set of common building definitions and room codes so that the reported data are reasonably consistent and comparable across the institutions and states. The manual provides the conceptual and definitional relationships for reporting on the major components of postsecondary facilities; namely, buildings and the use of space within those buildings. The manual facilitates the classification of the types of buildings and identifies detailed categories of room use through 24 definitions, classification systems and codes that describe and quantify building areas. Information and analyses derived from the data are necessary for the effective management and use of existing facilities, in planning for future expansions, and in budgeting for necessary maintenance and modification. The manual lists the recommended and optional data items for each building and room that should be incorporated in a facility inventory database (Refer to Appendix A for details on data items). It suggests that in designing the facilities database, recommended and optional data elements should be incorporated, even though not all the data may be collected at the outset. 2.3.2 COMBINE2 (Computer Models for the Building Industry In Europe) - Sub-Project Bulldlng HVAC Component Database (Talonpolka, et al, 1995) COMBINE is a research project focused on developing an operational computer based Integrated Building Design System (IBDS). It began with the development of new methods designated to enhance data sharing between a number of design applications in the fields of energy efficiency and building services engineering. The project enables better and more efficient building designs, especially from energy conservation, building quality, and heating and ventilation perspectives. COMBINE2 was the secOnd phase of the COMBINE project that focused on establishment of data structure and tools for managing the information exchange in a building design team. In the building heating, ventilation and air-conditioning (HVAC) component database subproject, a HVAC component database prototype was implemented that had a generic database 25 structure enabling it to store and manage any kind of project data and integrate these databases to building design systems. Researchers developed the HVAC component model using ER modeling techniques and implemented it with a Microsoft Access 1.0 relational database. The objective of this project was to build a generic data management and storage structure for integration of different manufacturer and supplier databases to facilitate the building design process (Talonpoika, et al, 1995). 2.3.3 Database for Facility Management Information System (Nawab, 1994) Nawvab (1994) developed a conceptual database model for a facilities management information system that can be used for estimating the utility KWH use in the University of Michigan’s academic buildings, forecasting the hourly load shape of the campus building operation, and simulating the impact of energy conservation strategies. The researcher developed a space model based on the Facilities Inventory and Classification Manual (FICM) published by the National Center of Education Statistics (NCES). The space model can be used for collecting and reporting data on college and university “buildings” and on space within those structures. Buildings and rooms were considered the two primary components of a facilities system. The data model suggests to institutions a pattern for essential data on their physical facilities and provides a set of common definitions and room codes for reporting data consistently and comparably across institutions. 26 For describing the real life Facilities Management lnforrnation System (F MIS), the conceptual model divided the database into three components (Figure 2.3). E??? TE": __ , ,_ {Simulation-m ..-_-- 7 Spatial Data A l . Entry na ysis Data - Modeling Conversion Information Environment IAT/GIS Data essence? Exchange -1. ,..-_ _ Figure 2.3 Conceptual Model Schema Source Nawab (1994) Data entry and data conversion are essential parts of the data input system. The simulation includes spatial analysis and modeling if linked to Image Associated Tools (IAT) or 3 Geographic Information System (GIS). The output could be customized through many data exchange programs for specific FMIS applications. The conceptual model provides a current and common framework of terms and definitions to build a data system on physical facilities, establish a system, and share information on a common platform. 27 The researcher also developed a logical model and a building information model. The logical model encompasses many different functional and organizational areas and contains information on utilities, communication, building equipment, and space utilization for the University of Michigan campus. The building information model stores information on building characteristics, assigned spaces, historical service records, and historical improvement. The relationships of the information were grouped in the four above stated categories. Other information that relates these groups was stored in data subsets to help the prediction model (eg. the building information and its classification, its space usage, equipment, computers, lights etc.). The logical and building information models were integrated and implemented in Oracle 7 relational database management software. Stnictured queries for prediction of values such as utility cost, KWH/sqft, KWH lighting etc., were designed for specific buildings and space groups. 2.4 Chapter Summary Management of buildings, utilities, and facilities are the key functions within energy management. Researchers have worked on each of these subsets of energy management and on integration of these subsets to achieve effective management. Building energy management research is focused on effective management of building equipment, energy retrofit, and structuring an effective energy organization. Researchers have utilized database technology for developing utility monitoring and metering databases (Lennig, 1995). The 28 National Center of Educational Statistics (NCES) published the Facilities and Classification Manual (FICM) that provides a common framework and coding structure to be used in collecting and reporting inventory data on college and university “buildings”, and on spaces within those structures. Based on the FICM coding structure, researchers in different universities have developed space management databases for keeping inventory of their university space data. Nawab (1994) developed a conceptual database model for facilities management information system; the database model was primarily a space model but also integrated energy related information on utilities with space utilization. 29 Chapter 3 Thesis Methodology This chapter describes the methodology used for developing an integrated energy management database model and focuses on the tools and methods that are used for data collection and analysis during its development. 3.1 Overview The thesis methodology is broken into five distinct steps including: a) literature review, b) data collection, 0) data analysis, (I) conceptual model development, and e) conceptual model validation. The literature review was done to collect information on research done in the area of energy and utilities management, and database modeling in facilities management. The literature review also focused on research pertaining to integration of utilities, space, equipment, and energy management, to describe existing research which was similar to author’ objective. The data collection process involved an email survey of Division I Research Universities’ energy administrators, designed to collect data on their existing energy management systems and future energy management needs. 30 The data analysis step included a qualitative analysis of the data collected from the survey of the Division I Research Universities. Data received from the energy administrators was entered in a spreadsheet and analyzed for patterns and trends. The conceptual data model was developed using Entity Relationship (ER) modeling techniques. Entity Relationship modeling was developed by information systems researchers for accounting information systems and database design. The ER diagrams organize data elements identified in terms of entities, relationships, and attributes. Data models for each of the subsets identified in the data analysis step were created and then integrated to develop an overall conceptual model. The validation of the integrated energy management conceptual model was done by conducting two case studies of Division I Research Universities. The first case study included structured interviews of an energy administrator in a representative Division I Research University and the second included an email survey of an energy administrator of another representative Division I Research University. 3.2 Data Collection Process An email survey of seventy-five (75) Division I Research Universities was conducted for collecting data on the existing energy management systems and 31 future energy management needs of the research universities. The survey, attached with an abstract of the research and a consent form, was sent by email to the facilities or physical plant director of the Division I Research Universities. Respondents completed and returned the survey either by email or tax. The email addresses of the facilities or physical plant directors were searched and obtained from the university’s web site, and an APPA website that has links to the facilities management websites of the member universities. A follow up email requesting participation in the data collection process, attached with the survey, consent letter, and thesis abstract, was sent to the energy administrators who did not respond to the first request. The survey was intended to gather data on: 1) physical characteristics of university campuses, 2) energy management trends, 3) organizational structures of the universities with respect to energy management, 4) energy monitoring and metering processes and methods used, 5) building details, 6) space data including documentation/storage, 7) space scheduling, and 8) data requirements for developing an integrated energy management database for institutional buildings. Data on the above mentioned areas were collected because researchers (Ferriera, 1999; Lennig, 1995; Qayoumi, 1999; and Navwab, 1994) have found these areas to be related to energy management of buildings, and should be integrated for better energy management. Integration of the different areas that effect energy consumption provide a better understanding of the problem areas and help in making decisions for achieving energy efficiency. 32 3.2.1 Survey Design The survey was broken into blocks of questions, based on similarity in process and information storage. Refer to Appendix B for the complete survey. The rationale behind each question was to collect specific information that was used to develop the conceptual database model, and is explained in sections 3.2.1.1 to 3.2.1.6. 3.2.1.1 Campus Physical Characteristics The universities that are classified in the Division I Research Category are placed there due to their similarity in various criterion such as research funding, student enrollment, degrees offered, campus settings etc. (Carnegie Classification). The physical characteristic data is important as it ascertains the campus size, number of buildings managed, and energy consumed. The number of buildings managed by the campus physical plant and annual energy consumption gives a basic understanding of the university’s physical size (i.e. an energy management system for a university with 25 buildings is different from a university that manages 500 buildings). The process of how energy is acquired by the university gives an understanding of the energy generation and/or procurement entities and methods. If a university generates its own energy, or generates as well as purchase energy from utility companies, than there need to be multiple entities In the database for the energy production, as the costs for each energy production unit can be different. 33 3.2.1.2 Energy Management, Organization and Trends The energy management structure of research universities varies according to the size, and physical and organization setup of the university. Data on the existing energy management organization structure and the current energy management trends gives an overview of the energy philosophy in various research universities. lnforrnation on the key department(s) responsible for energy management, and the information flow processes and patterns among those departments helps to provide an understanding of the overall energy management process within the research university. Data on energy management trends in various universities provides the formal and informal information about the efforts and plans that each university has taken for achieving energy efficiency on their campus. The questions designed to collect energy trend data are focused on whether there is an energy plan, was the plan developed with input from various members of campus community, if there is no plan what are the main reasons for not having an energy plan, listing of efforts made by the university to date for achieving energy efficiency, and the type and frequency of building energy audits done by the campus physical plant. 3.2.1.3 Energy Monitoring and Metering Energy monitoring for academic buildings is done in many different possible ways depending on the age of the building and energy management structure of the campus. Information on the type of monitoring system used is important for designing the database, as the level and type of systems vary. A basic system of 34 reading energy meters or analyzing fuel bills has a very low level of monitoring. Whereas a pneumatic based system used for energy monitoring can track a limited number of dedicated sensors, and may track the temperature and pressure for the major mechanical equipment. The newest form of energy monitoring systems known as Digital Direct Control system (DDC) uses programmable microprocessors for tracking energy. DDC microprocessors accept data from an almost unlimited number of sensors, or other microprocessors, and the control strategies are designed across pieces of equipment and systems to better coordinate control actions. Data on energy metering processes used on different campuses is essential for designing the database, as metering processes like monitoring, differs with the type of metering technology used. Digital meters connected to a computer give hourly as well peak load profiles; whereas with manually read energy meters, only monthly loads profiles can be obtained. lnforrnation on integration of energy monitoring and metering systems, if there is any, is vital as integration of the same is one of the sub-objectives of the database model. 3.2.1.4 Space Management Space management information is vital for an integrated energy management database as changes in use and space scheduling impact energy consumption, and to develop energy consumption benchmarks for different space types. Floor plans, building square footage, space utilization, space characteristics, and 35 space usage data are the typical data types that should be stored in a space management database. For integrating the space management data, like space utilization, space planning, and space programming, there is a need to know the existing process used for space management. One of the simplest forms of a space management procedure is assigning space utilization standards to CADD drawings. Another form of space management is the use of a database system and linking it to CADD files. The emerging form of space management is using the lntemet for storing and sharing space-related information. 3.2.1.5 Building Construction Data Storage Energy consumption in any kind of building is a function of the type of construction, thermal coefficients of building assemblies, and physical orientation of the building (solar heat gain/loss). Depending on the year of construction and design, different buildings have different construction assemblies and hence different thermal coefficients. To develop an integrated database, information on building construction details such as roof/waIl/foundation assemblies, thermal coefficients, and its form of storage is vital for the design. The most commonly used form of construction data storage is in blueprints, architectural or structural drawings stored in the campus physical plant. Most of the research universities existed before the advent of CADD systems; therefore, constniction details are stored in blueprints as well as CADD drawings. Another way of storing energy specific construction information is in spreadsheets or a database system. 36 3.2.1.6 Classroom Scheduling Most academic buildings in research universities are used as classrooms, laboratories, and offices. During a normal working semester offices and laboratories run on a fixed working schedule, but classroom occupancy in academic buildings is dependent on the number and schedule of courses scheduled for that particular room. In most universities, the mechanical systems in a building are scheduled (on/off times) based on the classroom schedules for that building zone. lnfonnation on departments that handle classroom scheduling and the due importance given to energy management when setting classroom schedules is essential as researchers have found out that making scheduling changes in a building is the most effective way of reducing energy consumption and is a low cost option. 3.3 Data Analysis The qualitative data analysis focused on characterizing the identified integrated database elements from the survey supplemented with information from the literature review. Database components were identified for each section (energy consumption, metering and monitoring, space management, building characteristics, and classroom scheduling). For each identified section, data elements were grouped into areas, and these areas were used to derive abstraction level entities for the ER model. This step established the requirements of the integrated energy management database, and set a foundation for the conceptual data model development. 37 3.4 Conceptual Model Development The focus of this step is to develop an integrated energy management database conceptual model. Conceptual models are tools for representing reality at a high level of abstraction (Batini, et al., 1992). Therefore, it is important to abstract the description of all data components and the logical structure of the integrated energy management system. In this research, Entity Relationship (ER) modeling was used as the tool for developing the conceptual model. The ER modeling method was used as the database-modeling tool because its readable and simple format is well recognized, and available in both computer science and information systems literature. The identified data elements from the data analysis section were organized in terms of entities, relationships, and attributes using ER modeling techniques. Data models for each of the areas/subsets identified in the data analysis step were created and then integrated to develop an overall conceptual model. 3.5 Conceptual Model Proof of Concept The proof of concept of the integrated energy management conceptual model was done by conducting two case studies of Division I Research Universities. The case studies were focused on collecting information on the potential feasibility and adaptability of the conceptual integrated energy management model as a whole, and in their current university settings. For the first case study, 38 a validation packet including the thesis abstract, the simplified integrated energy management conceptual model, and the consent form were sent to the interviewee by email prior to the interview. During the interview, the author explained the processes and methods that were used to develop the model to the interviewee and collected their feedback. For the second case study, a validation packet including the thesis abstract, the simplified integrated energy management conceptual model, and the consent form was sent to the energy administrator by email. The email method was used because conducting a personal interview was financially not viable for the author, as the physical location of the identified Division I Research University was not in close proximity. Energy administrators were asked to review the validation packet and send their feedback on the feasibility and adaptability of the model as a whole in their current university setting. The second case study was done to give a generic sense to the developed integrated energy management conceptual database model. Feedback and suggestions from the energy administrators ware discussed as the area of future research in Chapter 6. 3.6 Chapter Summary This chapter provided an overview of the methodology used in this thesis. The chapter emphasized the steps and methods used for data collection, data analysis, conceptual model development, and conceptual model validation. 39 Chapter 4 Data Reporting and Analysis This chapter reports and presents the analysis of the data collected from the survey of the Division I Research Universities to identify the requirements of the integrated energy management database. 4.1 Data Reporting An email survey of Division I Research Universities was conducted for collecting data on the existing energy management systems and future energy management needs of the research universities. The survey, attached with an abstract of the research and a consent form, was sent by email to the facilities or physical plant director of the seventy-five (75) Division I Research Universities. Six respondents completed and returned the survey either by email or fax. A follow up letter with attachments requesting participation in the survey was sent to the respondents who did not respond to the earlier request. The response rate of the survey was low, but the author feels that it did not effect the model as the six responses received were consistent with the requirements of an integrated energy management system and the survey was more qualitative in nature. Due to a low response rate of survey participation an email requesting reasons for non-participation was sent to twenty-four (24) energy administrators who did not participate in the survey. Of twenty-four, five energy administrators replied to the 40 email request providing reasons for non-responsiveness to the earlier email survey. The primary reason for not responding was lack of time and type of information requested. All the five energy administrators said that they thought the survey would take more than 15 minutes to complete and they did not have enough time. Three of the respondents said that the information requested was not readily available, and they did not have the time to search where the required information was stored. The data reported in this section represents data from the six universities that responded to the survey. 4.1.1 Campus Physical Characteristics The number of campus buildings managed by the six university physical plants range from 130 to 638, the average number of buildings being approximately 225. According to the 1997-98 Comparative cost and staffing report (APPA), the average Gross Square feet (GSF) managed by the research universities was approximately 6.16 million square feet. The average energy consumption for the previous year in Division I Research Universities was approximately 1,846 billion BTU (British thermal Unit) and the average annual percentage increase in energy consumption over the last five years was approximately 7%. Of the six universities, two universities acquired energy only through utility companies and the other four universities acquired energy through utility companies and campus power plants. Of the six universities, one had an operational cogeneration power plant and one university was in the process of converting its existing power plant to a cogeneration unit. 41 4.1 .2 Energy Management Trends Two universities had an organized energy management plan or reduction target in place and both of them had incorporated facility modifications, utilization or schedule changes, and operating changes in their energy management plan. Both these universities also conduct energy audits of campus buildings periodically, but not on a set schedule. For the four universities that did not have an energy management plan in place, energy consumption had been increasing over the last five years. The main reason stated by the energy administrator for not having an organized energy plan was that energy management was not one of the high priorities for upper university administrators. Regardless of whether the universities had a formal energy management plan or not, all of them had taken some steps to achieve energy efficiency in their campus buildings. The steps taken to date for achieving energy efficiency focused on technical retrofits including lighting retrofits, chilled water looping systems, insulation, HVAC controls, and preventive maintenance of HVAC systems. One of the universities had also made equipment-scheduling adjustments to match occupancy patterns. 4.1.3 Organization Structure The campus physical plant or facilities management departments were responsible for energy management on all the six universities surveyed. The common process used by the universities for communicating energy management related information from other departments was periodic reporting 42 of cost, billing data, and metering data. Two of the universities had an automated energy system that collects and manages the energy data and billing functions. 4.1.4 Energy Monitoring and Metering All the six universities surveyed had a system to monitor energy consumption for their campus. The different energy monitoring methods used by these universities were recording meter readings every month, using pneumatic based systems, or using computerized energy management systems also known as digital direct control (DDC) systems. Utility/fuel bills and meter readings were the most common methods for monitoring energy. Three universities used a computerized energy management system in addition to meter readings and utility bills for monitoring energy consumption. These three universities also had systems in place that integrated energy monitoring data with energy production, distribution, and metering data. The various integration processes adopted were storing steam production and consumption data in a database, storing meter readings in a locally developed MS Access database, and storing utility bills in a database and importing data electronically through electronic data interface (EDI). 4.1.5 Space Management All six universities surveyed maintained databases for recording facility space utilization information. Three of the six universities had a system in place to integrate the space utilization data with their energy management system. Of the 43 three universities that did not have such a system two of them thought there is a need to integrate this information to identify potential energy saving areas and make energy management decisions. One of the university administrators thought that there is no need for integration, because at university, buildings are classified by occupancy (such as research, instructional, administrative, and laboratories) and energy use for buildings with the same occupancy classification are compared. 4.1.6 Bulldlng Characteristics The campus physical plant or the facilities management department stores data on building physical characteristics such as wall, roof, and foundation construction assembly details. All six universities use blue prints or paper files to store building characteristic data. Only one of the six universities uses both paper files and a database management system to store building characteristics. Of the five universities that do not have a database management system to store building information, four universities believe that there is a need to store the information in a database. 4.1.7 Classroom Scheduling The Office of the Registrar or other university departments outside the physical plant manages the classroom scheduling process for all the surveyed universities. All the six energy administrators believe that due importance to energy efficiency is not given while setting up the classroom schedules and there is currently no system in place that integrates the classroom scheduling data with the mechanical equipment schedules. 4.1.8 Research The amount and type of research conducted in buildings effect energy consumption to a large extent and hence should be a part of an integrated energy management database. A building serving research purposes will have higher energy consumption than a building that is seldom or not used for research. The type of research also effects the energy consumption in a building. Research involving heavy equipment and machines are more energy intensive than the research that involves less or no equipment. 4.1.9 Integrated Energy Management Database Components Five of the six universities responded to question 32 of the survey that lists the components to be incorporated in an integrated energy management database. All five respondents believe that energy consumption data should be included in the database. For energy metering, energy monitoring, mechanical equipment, space scheduling, and space utilization the response was varied. None of the energy administrators were sure about inclusion of building construction details and space characterization details in the database. Refer to Figure 4.1 for details on the database elements that should be incorporated in an integrated energy management database. The number in each cell signifies the number and type of 45 response for that particular database component. For example, for energy metering data the response was four “yes” and one “unsure”. Table 4.1 Integrated Energy Management Database Components Database Components Yes May Be Unsure No Energy Consumption Data 5 Energy Metering Data 4 1 Energy Monitoring Data 2 2 1 Mechanical Equipment Data 2 2 1 Building Construction Details 4 1 Space Scheduling Details 3 1 1 Space Utilization Data 2 1 1 1 Space Characterization 2 2 1 Details Weather Normalized Data 1 4.2 Data Analysis This section analyzes the data collected from the surveys supplemented with existing literature for identifying the database components in an integrated energy management database model. For each section (energy consumption, metering and monitoring, space management, building characteristics, and classroom scheduling). data elements are identified and grouped into areas. 46 These areas are used to derive abstraction level entities for the Entity Relationship (ER) model. 4.2.1 Campus Physical Characteristics, Organization Structure 8. Energy Management Trends The number of buildings managed by the campus physical plant is varied, hence the database should be flexible in handling the number of buildings. The campus physical characteristic data indicate that energy is either produced by the campus physical plant or purchased by utility companies, or a combination of both. Therefore both campus power plants and utility companies are an area in the integrated energy management database. The campus power plants produce steam and electricity, and the type of power plant and its efficiency determines the unit energy costs. The utility companies supply steam and electricity to the universities and have a predefined unit cost for both steam and electricity. The energy meters on the supply side measure the amount of steam and electricity supplied. Energy consumption is measured in different units in each university, therefore to develop a generic database all energy units need to be converted to BTU'S, as it helps in comparing different energy data within and among other universities. Utility costs in a university are a factor in determining average unit costs. Steam and electricity are the only energy forms considered in this database model. Other forms of energy such as natural gas could be added to the database but for concept simplicity have not been included in the conceptual model. 47 Table 4.2 Data Areas and their Items for Campus Physical Characteristics Areas Data Items Campus Financial Year, Plant Name, Plant Steam Capacity, Plant Power Electricity Capacity, Actual Electricity Produced, Actual Steam Plant Produced, Plant Type, Plant Description, Unit Steam Generation Cost, Unit Electricity Generation Cost, Plant Efficiency, Location Utility Financial Year, Utility Company Name, Location, Actual Company Steam Purchased, Actual Electricity Purchased, Unit Steam Purchasing Cost, Unit Electricity Purchasing Cost 4.2.2 Energy Monitoring, Metering and Consumption Energy monitoring is one of the most important components of a successful energy management system. lnfonnation on the type of monitoring system used is useful in designing the database as the level of monitoring and system details vary. Of the six universities, three used a computerized energy management system whereas the other three used meter readings for energy monitoring. Two of the six universities surveyed used their energy monitoring system for metering too; the other four universities were reading meters manually on a fixed schedule. According to the author’s knowledge of energy management systems, energy metering, and literature review (Lenning, 1995 and Qayoumi, 1999), the 48 data elements that can be derived from the energy monitoring system analysis are listed in Table 4.3. These data elements are grouped into areas, and the areas are used to derive abstraction level entities for the ER model. Table 4.3 Data Areas and their Items for Energy Monitoring System Area Data Items Energy Monitoring Equipment ID, Type, Description, Location Monitoring Electricity Meter ID, Description, Location, Meters Electricity Meter ID, Date Read, Meter Reading, Conversion Factor (If Meter Any), Btu Equivalent Reading Steam Meters Meter ID, Description, Location, Steam Meter Meter ID, Date Read, Meter Reading, Conversion Factor (If Reading Any), Btu Equivalent 4.2.3 Building Equipment Data HVAC and electrical equipment are the major energy consumers in any commercial building, hence one of the most effective energy efficiency measures are improvements in the HVAC systems. The reason for the significance of HVAC is that air conditioning is the fastest growing element of energy use in the 49 US and most air conditioning systems rely on electric power for their operation and control (CRC Handbook, 1997). According to the author, the improvements in system operation and increased energy efficiency in any building can be best achieved by storing building equipment data in a database. Of the six universities surveyed energy administrators of two universities agreed that building equipment data should be incorporated in an integrated energy management database. The data elements that can be derived from the literature review, authors knowledge and survey analyses are listed in Table 4.4. Table 4.4 Data Areas and their Items for Building Equipment Area Data Items Equipment Equipment ID, Description, Type, Capacity, Energy Consumption Rate, Semester, Date, Start Time, End Time Equipment Equipment Category ID, Equipment Category Description Category Equipment Equipment ID, Semester, Date, Start Time, End Time Schedule 4.2.4 Space Characteristics All six universities surveyed maintained a database for recording facility space utilization information. The space management database for any institution is more than likely to be based on the Facilities Inventory and Classification Manual 50 (FICM) published by the National Center for Educational Statistics (NCES) (Korb, R, 1992). According to the Facilities Classification and Inventory Manual, buildings and rooms are the two primary components of a facilities inventory system. Rooms are further grouped by use category, functional use, and institutional unit. The FICM suggests typical data elements for storing building and room information in a space management database (Refer to Appendix A). Higher education institutes are involved with a variety of research that effect energy consumption and hence research indicators should be a part of the space model. The data elements that can be abstracted from the space management database are listed in Table 4.5 51 Table 4.5 Data Areas and their Items for Space Characteristics Area Data Items Building Building ID, Building Name, Year Of Construction, Gross Area, Assignable Area, Total Cubic Feet Volume, Location, Building Owner, Type Of Construction Building Functional Use Building Functional Use Code, Building Functional Use Description Maintenance Log Maintenance Log #, Maintenance Log Description, Date, Comments Research Functional Research Functional Use Code, Research Functional Use Use Code Descriptions, Yearly Research Dollars Spent, Yearly Research Man-Hours Accumulated Room Room ID, Room Number, Room SF Area, Room Use Category, Capacity, Floor, Lighting Load, Office Equipment Load Room Use Category Room Use Category ID, Room Use Category Description Room Functional Use Room Functional Use Code, Room Functional Use Code Description Institutional Unit Institutional Unit ID, Institutional Unit Description 52 4.2.5 Space Scheduling Details The most important space type for scheduling purposes is classrooms, as they are used only when classes are in progress. Other space types like offices, laboratories, computer labs, and circulation spaces operate on a set schedule. For all the six surveyed universities, the Office of the Registrar or other university departments outside the physical plant manage the classroom scheduling process and all the six energy administrators believe that due importance to energy efficiency is not given while setting up the classroom schedules. Integration of space scheduling data with equipment scheduling data is one of the most advantageous energy planning strategies in building energy reduction (Nawab, 1994 & Ferriera, 1999). Three of the six survey respondents agreed with the inclusion of space scheduling data in the integrated energy management database model. The data elements that can be derived from the literature review, space management data analysis, and classroom scheduling analysis are listed in Table 4.6. 53 Table 4.6 Data Areas and their Items for Classroom Scheduling Area Data Items Classrooms Room ID, Room Number, Room Capacity, Room SF Area, Lighting Load, Office Equipment Load Course Course ID, Department, Student Enrollment For The Scheduled Course Course Course ID, Semester, Date, Start Time, End Time Schedule 4.2.6 Building Construction Details Building physical characteristics such as wall, roof and foundation construction. assembly details, and their thermal coefficients affect building heat loss/gain and hence energy consumption. Only one of the six universities uses both paper files and database management systems to store building characteristics. Of the five universities that do not have such a system, four believe that there is a need to store the lnfonnation in a database. The area and database elements that can be derived from the literature review and analysis of the survey are listed in Table 4.7. 54 Table 4.7 Data Areas and their Items for Building Physical Characteristics Area Data Items Construction Assemblies Assembly ID, Description Walls Assembly ID, Average “U” Value For Wall, Year Of Construction, Gross Area, Wall Description Fenestration Assembly ID, Average “U” Value For Fenestration, Year Of Construction, Gross Area, Fenestration Description Basement Slabs Assembly ID, Average “U” Value For Basement Slab, Year Of Construction, Gross Area, Basement Slab Description Roof Assembly ID, Average “U” Value For Roof, Year Of Construction, Gross Area, Roof Description 4.3 Chapter Summary In this chapter, the data collected from the survey of Division I Research Universities was analyzed. Information requirements for the integrated energy management database were examined. Data elements in each section of the survey were identified and classified by dividing them into areas. The classification of areas as data elements established the approach towards abstraction of entities for the conceptual database model. In the next chapter, the conceptual integrated energy management database is formalized. 55 Chapter 5 Integrated Energy Management Database Model Information requirements in an integrated energy management database were analyzed in Chapter 4. After data elements are identified and classified, the next step is database model development. This chapter explains the development of the conceptual database model. The Entity Relationship (ER) model was used as the modeling method. ER models organize the data in terms of entities, relationships, and attributes. 5.1 Integrated Energy Management Database Development Process Database development is a subset of an overall Information system (IS) design process. An lnfonnation system (IS) collects, processes, store, and disseminate information for a specific purpose (Turban, McLean, and Weatherbe, 1999). Like any other system, an IS includes inputs (data, instruction) and outputs (reports, calculations). Refer to Appendix C for details on information system concepts, database systems, and logical data organizations. The lnfonnation systems development cycle consists of five distinct steps: a) system assessment, b) system analysis, 0) system design, d) system implementation, and e) system administration. Refer to Appendix D for a comprehensive discussion of Information systems. During the system design phase, entity relationship models are used to draw the conceptual data model for the proposed database. There 56 are two main kinds of database design methodologies that reflect the design philosophy of an lnfonnation system design: process driven design and data driven design (Maciaszek, 1980; Raymond, 1987; and Nijssen, 1989). In this research, the data driven design methodology is used for development of the integrated energy management database. The data driven design methodology is focused on what kinds of data are stored in the database and the interrelationships and constraints among various data. It builds the data structure based on general semantics of information used in business processes. The main task is to build a data representation model of the objective system, focusing on data and its properties. Once the data are identified and structured in a particular format, data storage and networking procedures are elaborated for proper design of the database. Refer to Appendix E for details on database design methodologies. The development process for the integrated energy management database model is shown in Figure 5.1. The left column shows the systematic phases in database design process. The middle column lists the tasks needed to be completed In each phase, whereas the right column lists the particular methods used in this research for development of the integrated energy management database model. Refer to Appendix F for more details on database design processes. 57 Database Design Tasks Methods used in Integrated Process Energy Management Database Modeling Information Objective & 0 Conduct & analyze Requirement Scope survey of Division I Analysis Business model research universities development energy management Define data and system business rules 0 Literature Review Conceptual . Develop ER Desrgn Conceptual data model(s) model - Integration of development various independent models Logical D . esrgn Translate conceptual model into tables and views Physical Define storage Desrgn structure Interface Figure 5.1 Integrated Energy Management Data Model Development Process 58 5.2 Entity Relationship Database Modeling A conceptual database model is an important concept in database design. It describes the structure of a database in a conceptual schema, independent of the particular DBMS. A typical database model shows all data and their relationships. If the models are not logically sound, the database designs derived from them will not deliver the database system’s promise of effective information drawn from an efficient database (Rob & Coronel, 1997). An Entity-Relationship (ER) approach for conceptual database modeling was introduced in 1976. Several conferences have been organized on the applications of an ER model to database design and to software design in general (Batini, et al., 1992). The basic concepts provided by an ER model are entities, relationships, and attributes. - Entities represent classes of real-world objects. They are the principal data objects about which information is to be collected (T eorey, 1994). A particular occurrence of an entity is called an entity instance. In an ER model, entities are represented by a rectangle containing the entity’s name (Figure 5.2). 0 Relationships represent real world associations among one or more entities, and as such, have no physical or conceptual existence other than that inherited from their entity associations (T eorey, 1994). A particular occurrence of a relationship is called a relationship instance. In an ER model, relationships are represented by diamond shaped symbols (Figure 5.2). 59 o Attributes represent elementary properties of entities or relationships (Batini, et al., 1992). There are two types of attributes: identifiers and descriptors. An identifier (or key attribute) is used to uniquely determine an instance of an entity; a descriptor (or non key attribute) is used to specify non-unique characteristics of a particular entity instance. In an ER model, they are usually represented by a line and a circle connecting to the entity box or relationship diamond symbol. Relationships in ER models are described in terms of degree, connectivity, and cardinality. - Degree of a relationship indicates the number of entities associated in a relationship (Teorey, 1994). A unitary relationship exists when an association is maintained within a single entity. A binary relationship exists when two entities are associated. 0 Connectivity is used to describe the relationship classification. It includes one-to-one, one-to-many, and many-to-many relationships between entities (Rob & Coronel, 1997). o Cardinality expresses the specific number of cardinality occurrences associated with one occurrence of the related entity (Rob & Coronel, 1997). It describes the number of instances that are related through a relationship. In an ER model, numbers in parenthesis represents cardinality. These concepts are illustrated in Figure 5.2. lnsthctor and Course are two entities. Course ID, credit hours, and number of students are the attributes of 60 entity “course” whereas employee number, name and class taught are the attributes of entity “instructor". The attributes course ID and employee number are the primary keys for the entities course and instructor respectively and are represented with a line with bold circle at the end. Their relationship is “take class” which means instructor takes courses. Its binary relationship has one-to- many connectivity. Cardinality of this relationship is shown in parenthesis. On the course side, the left side “1” means each course has to be taught by a minimum of one instructor and the right side “1” indicates that a course can be taught by a maximum of one instructor. On the instructor side, “0” means that an instructor can teach no courses and “n” indicates that an instructor can teach a maximum of n courses. 61 / Entity Relationship Entity (1.1) Take (0.N) Course Class _—_- Instructor Course ID 0 Employee le'l'lbel‘l a Credit hours 0 Class taught Number of Students 3 Name Figure 5.2 Entity Relation Model Example 5.3 ER Template to Model Identified Elements In this research, a top down approach and decentralized design option is used for development of the conceptual integrated energy management model (Refer to Appendix G for details). The subsets that are integrated to form the overall conceptual model are campus physical characteristics, energy monitoring and metering, building equipment, space characteristics, space scheduling, and building construction details. A conceptual data model for each of the subsets are created and then integrated to develop an overall conceptual model. 62 5.3.1 Campus Physical Characteristics The key energy entities that effect energy consumption in a university are buildings, campus power plant and utility companies. The ER model of campus physical characteristics records data on how buildings are related to campus power plant and utility companies. Buildings in a university can acquire energy from the campus power plant and/or utility companies. It is assumed that a particular building can either receive energy from the power plant or utility companies but not both. An ER model with the campus power plant and utility companies delivering energy to buildings is shown is Figure 5.3. The cardinality in each relationship is shown in parenthesis in Figure 5.3. The campus power plant or utility companies can supply energy to a minimum of zero buildings and a maximum of “n” number of buildings. A building’s energy can be supplied either by the campus power plant or a utility company, and it can be supplied by a maximum of one campus power plant or utility company. 63 (0. N) Campus Power Plant Supply to (0. 1) Bulldogs (0. N) .\ Ub‘lity Cornapanies tpply to (0. 1) Figure 5.3 ER Model for Campus Physical Characteristics 5.3.2 Energy Monitoring & Metering Energy metering and monitoring systems for an institutional building includes steam meters, electricity meters, and energy monitoring equipment. Analysis of the survey of Division I Research Universities indicate that energy monitoring in research universities is either done by steam and electricity meters read manually or by a digital direct control (DDC) system connected to steam and electricity meters. An ER model for energy metering and monitoring systems (Figure 5.4) shows steam and electricity meters connected to buildings and energy monitoring equipment. The cardinality in each relationship shown is in parenthesis in Figure 5.4. A building can have a minimum of one steam/electric meter and a maximum of “n” steam/electric meters. A steam/electric meter is not 64 necessarily connected to the energy monitoring equipment and it can be connected to only one piece of energy monitoring equipment. (1.1) SteamMeteis (1. N) Buildngs (0. N) (1, N) M... 4...... V» Figure 5.4 ER Model for Energy Monitoring and Metering Systems 5.3.3 Building Equipment In institutional buildings, the major elements of building equipment can be categorized as heating, ventilating, air conditioning, and electrical equipment. As the nature and functionality of building equipment is varied, the equipment category helps in grouping similar types of equipment. Each equipment element 65 belongs to one of the defined equipment categories. HVAC equipment in a building can be monitored manually or through remote energy monitoring equipment. Each piece of HVAC equipment item has an operational schedule depending on the building or zone operation schedule. Figure 5.5 shows an ER model with entities and relationships for building equipment. The cardinalities for each relationship are listed in parenthesis. EnergyMonitorlng Wm (1. N) (1.1) (1.1) (1. N) Equipment ‘ Btildrig . . Si ”8 E i I Consistsof Buldngs (1. 1) Figure 5.5 ER Model for Building Equipment 5.3.4 Space Characteristics In research universities, buildings are used for varied functions such as research, academic, healthcare, instructional, etc. To develop benchmarks and compare data, buildings are grouped based on functions. Buildings typically contain numerous rooms used for a variety of purposes. Most of the detailed data on how 66 space is used, by whom, for what purposes, and other important variables are linked to the inventory of rooms (or assignable space). All rooms in a research university are allocated to one of the room use categories. Refer to Appendix A for room use categories. For achieving benchmarking data and comparing performance data, rooms are grouped based on functions performed. Each room in an institutional building belongs to an institutional unit that manages the space. Figure 5.6 shows an ER model with entities and relationships for building space characteristics. The cardinalities for each relationship are listed in parenthesis. Rmetndimd Bjti'gu$8 lbs (0. M (0N)o (1 1) (1,1) (1 1) (1.M ‘ mt ‘ a]. (0. M (1. 1) (1. 1) (0. M [m i (0. M (1. 1) erane Wm my LCD Ftndiad Lbs Figure 5.6 ER Model for Building Space Characteristics 67 5.3.5 Space Scheduling The most important space type for scheduling purposes is classrooms, as they are used only when classes are in progress. Other space types like offices, laboratories, computer labs, and circulation spaces operate on a set schedule. Each classroom is a type of room and has at least one course scheduled in it. Each course has a predetermined schedule that determines the room operation schedule. Figure 5.7 shows the relationships and cardinalities between entities in a space scheduling ER model. (0. N) (1. 1) (1.") (1. 1) (1.1) (1.") Figure 5.7 ER Model for Classroom Scheduling 5.3.6 Construction Details An abstraction is a mental process that is used to select some characteristics and properties of objects and to exclude other irrelevant characteristics. Aggregation is one type of abstraction concept used in database modeling. It defines a new class from a set of other classes that represents its common parts 68 (Batini, et al, 1992). Aggregation is described as a “part of” relationship; for example, wheel, pedal, and handle bar are all parts of a bicycle. The aggregation concept is illustrated by the relationship between the construction assemblies of walls, roofs, fenestration, and basement slabs. Each construction assembly may have wall, roof, fenestration, and basement slab. This relationship is shown in Figure 5.8. l l l Walls l (1.1) i Typeof (0.N) I l l , (0.N) (1,1) } I Construction ’ ‘ Buidrngs 1 (Have l llies I Typeof —— Roof I ; (1.N) ~ (1.1) 1 «w I . l I Typed (o,N) , .. (1.1) ' 7 V. Typeof (1.1) i l . l l Basement ‘ I Fenestration Slabs ; Figure 5.8 ER Model for Construction Details 69 5.4 Overall Conceptual Model From the analysis of the different energy management components, an overall conceptual model can be derived. Figure 5.9 illustrates the entities and structure of an ER model for an integrated energy management database. The model is derived by connecting logic relations, which were explained above in Figure 5.3 to Figure 5.8. The ER model records data on energy characteristics of campus buildings. The campus power plant or utility companies deliver energy to each campus building. The ER model stores data on type, number, and schedule of equipment in a building. It also tracks which elements of equipment are connected to the energy monitoring system. The ER model stores data on steam and electricity metering for each building. Data on building construction details like wall, roof, fenestration, and basement slabs are also stored. Space characteristics such as room use category, institutional units, and space functional use are integrated with building energy consumption data for developing benchmark data. Student enrollment and classroom scheduling data are also incorporated and integrated with building equipment data. The conceptual database model represents an abstract picture of an integrated energy management database. Actual energy production and consumption data can be recorded and compared. In addition, time related energy data is also included for comparing energy consumption over the years and making strategic decisions for achieving energy efficiency in campus buildings. Each entity in Figure 5.9 has its attributes to describe the data characteristics of the entity. All 70 entities in Figure 5.9 and their attributes are listed in table 5.1. The key attributes for each entity is underlined. 71 hogan—80w Q 09:30 i 82 .88 NR _cuoE 338%. EeEeuncuE 86.320 833.535 83:83:00 m. m 952.... Peas-U 3:588:85 Sauugum 30888388.".— 1 116228 28:28an lillii »u._o.=m 82 .8 .1 .. ....1..o>-/1. 82 .88 z. . I: .8 3 .8 18.9.22... 2.1.: 82.8 1.11.1/11ll| I .. Iii III- . o. o. _ :. .8 c. .8 23.88.18. £32269 32.50 i... .I 11.1H1I .. . |111H 2 .8 3 .8 88.88..=uoE.818.=.8.m 3 ‘08? iii III. III ii II. 1.1 =838=m11il11/.1.1.1n:8v-o¢- . 8 8 €802 €302 2:: . ii 1.30:. .. Iii l1 III 88 .8 z .0 5.08m 8988.81.30.88 8989:3938 r1 2:8 vouoto 1 . 1 I l. 82 .88 . ii... iii. ///1..... 1.111 fl 3 .8 . 82.: 3.: 2. 88 1!.I. I # 82 88 1 Ii-[1l 0:8811/ on: _ . 9:: - I1 823835.... .I! II I 111‘ 8p .88 /\ 0830/ xlxi—ii: Ig/ AZ .08 souaonox a... no I 1. HEOOHnuC—U ‘ —1 h0-0E‘E.¢U~MI‘ Az.Fv1IIIIIII / II Illi 1111 1 1 III ll. 82 .8 1l. . II o>u818 .11./ill i cocoa—mp?! 3 88 / lli. .. . a / 3 .88 I i :i-..l I I I I I. .i/Kuo 2.82.001... / 2 .8 . . .// 82.8 I. .1Il1 . III .11.. :3..— 330.8 I ll 1 riifl . e .1. IIIIIi. III|I/°d A—th=m /1i 1 .III Iq—QECU I l I I- i. ...1 3 88 .. / 82 .8 1 -.li.. 353:... i. 1 /. ..1 . 8 / 8| lili- cured—”8%....“ 1 I I 2.30: 1.11.1l ll ..lI/Q/o Satay III. I 1_I8z 8.8 88 .8 .11../// 82 .8 ..III II. . .. no :8an l 1 1 1 :.: l l. . /1. . -éaaamyilsi m”.55 c 1!. dz: 1 _ 82 .8 / 3.88 ..1-1—3.88 1- I .i . l. . - I. .. II 111:8il1l I- 1 . I11/19/>.._.= /. .z. 8 / 2. .8 3 88 383503< 1i II .8» /I|.I|i 3 .88 / 1ll 11 ill- c9333.... I1/181d o .8... 2:3 // on: . U / . . o. 3.228 / 3:289:38 . II .1I1|81I-l. .. ll 1 /. /, .1. . —iu=8w_8.=m 8 8 82 .8 8 0:8: ./ .. !1 z .o // .. . ..1h......8o 0.88.8.1... . III . 82.8 1 . .z 8 8 . 8 82.8 ./ S ..8 I. ..... i It 8 «iii 8i. 1 I /. .8Ooaxh//..1.I1. .1 32m lo..:1I 1. I. l 2 ..8 . . ./ // . .. .5533 88:3 . 1 ll .. 1/ . .. II. Izi fl .uchmM“=n8 ii 1:082:88...— 8ee~8 /8ouax._.1111 sot-8.3.3.18 Table 5.1 Entities and their Attributes Entity Name Attribute Campus Financia_l Year, PlaLt m, Plant Steam Capacity, Plant Power Plant Electricity Capacity, Actual Electricity Produced, Actual Steam Produced, Plant Type, Plant Description, Unit Steam Generation Cost, Unit Electricity Generation Cost, Plant Efficiency, Location Utility Financia_l Year. U_tility Qo_mpanv Name, Location, Actual Steam Company Purchased, Actual Electricity Purchased, Unit Steam Purchasing Cost, Unit Electricity Purchasing Cost Energy Monitoring Eguipment ID, Type, Description, Location Monitoring Electricity EILctricitv Meter ID, Description, Location Meters Electricity file—ctricity Meter lQ,_Date ReadL. Meter Reading, Conversion Meter Factor (If Any), BTU Equivalent Reading Steam Steam Meter ID, Description, Location, Meters Steam Meter Reading Steam Meter Id Date Rea_d. Meter Reading, Conversion Factor (If Any). Btu Equivalent 73 Table 5.1 (cont’d). Entity Name Attribute Equipment Equipment ID, Description, Type, Capacity, Energy Consumption Rate Equipment Equipment Category ID, Equipment Category Description Category Equipment Equipment ID, Semester, Date, Start Time, End Time Schedule Building Building ID, Building Name, Year Of Construction, Gross Area, Assignable Area, Total Cubic Feet, Location, Building Owner, Type Of Construction Building Building Functional Use Code, Building Functional Use Functional Description Use Research Research Functional Use Code, Research Functional Use Functional Code Description, Yeariy Research Dollars Spent, Yearly Use Research Man-Hours Accumulated. Maintenance L09 Maintenance Lm #, Maintenance Log Description, Date, Comments Room Room ID, Room Number, Room SF Area, Room Use Category, Capacity, Floor, Lighting Load, Office Equipment Load 74 Table 5.1 (cont’d). Entity Name Attribute Room Use Room Use Categom ID, Room Use Category Description Category Room Room Functional Use Cfiode, Room Functional Use Code Functional Description Use Institutional Institutional Unit ID, Institutional Unit Description Unit Classrooms Room ID, Room Number, Capacity, Room SF Area, Floor, Lighting Load, Office Equipment Load Course Course ID, Department, Student Enrollment For The Scheduled Course Course Course ID, Semester, Date, Start Time, End Time Schedule Construction Assembly ID, Description Assemblies Walls Assembly ID, Average “U” Value For Wall, Year Of Construction, Gross Area, Wall Description Fenestration AssemQILIQ, Average “U” Value For Fenestration, Year Of Construction, Gross Area, Fenestration Description 75 Table 5.1 (cont’d). Basement Assembly ID, Average “U" Value For Basement Slab, Slabs Year Of Constmction, Gross Area, Basement Slab Description Roof Assembly ID, Average “U” Value For Roof, Year Of Construction, Gross Area, Roof Deschion 76 5.5 Logic Design Logic design translates the conceptual model into a particular database management system (DBMS). In a relational database environment, tables are created using Structured Query Language (SQL). This language is designed to work with any application that requires the manipulation of data stores in a relational database (Rob and Coronel, 1997). By using SQL, table structure can be created within the designated database. Most DBMS now use query by example (QBE) interfaces that allow the attribute names to be typed into a template and the attribute’s data type to be selected from a drop down menu. 5.5.1 Table Creation All entities in the database conceptual model can be implemented in relational tables. In one-to-many relationships, there are two ways of applying relationships in the conceptual model to a DBMS. The primary key in the one-side entity can be posted to the many-side entity to become a foreign key attribute, or an additional table with primary key of both relationships can be created to represent the relationship between entities. There is only one way for modeling a many -to- many relationship, by creating a separate table and posting the primary key of both the entities. In database implementation, each of the relationships is implemented by creating a table. Each of the entities shown in Table 5.1 becomes a table in database implementation. Some of the data table structures are shown Table 5.2 to Table 5.4 Refer to Appendix H for details on all the tables in an integrated energy management database. In each table, the primary keys 77 are underlined and the posted keys are marked in italics. The first column lists the field name and the second column lists the data type for that field. Table 5.2 Building Data Table Field Name Data Type Building ID Number Building Name Text Year Of Construction Date/Time Gross Area Number Assignable Area Number Total Cubic Feet Number Location Text Type Of Construction Text Financial Year Date/1' ime Utility Company Name Text Power Plant Name Text Maintenance Log # Number Research Functional Use Code Text Building Functional Use Id Number 78 Table 5.3 Electricity Meter Data Table Field Name Data Type Electricity Meter Id Number Description, Text Location Text Energy Monitoring Equipment Id Number Building ID Number Table 5.4 Building Equipment Data Table Field Name Data Type Building Equipment ld Number Description Text Type Text Energy Consumption Rate Number Capacity Number Monitoring Equipment Id Number Building ID Number Building Name Text Equipment Category ld Number 79 5.5.2 Standard Queries and Reports The database tables when implemented in particular database management software can be used to create standard reports using SQL procedure. Although data are stored in different tables, they are related to each other by sharing common entity characteristics, a foreign or posted key attribute. The logical relationships link the tables together to become a central data pool. Query is the tool to draw data from the pool to meet the information needs. Reports on monthly and yearly energy consumption, benchmarking data based on building functional use, building lighting loads etc can be generated by the use of an integrated energy management database model. Refer to Appendix J for details on a few queries that can be generated by the database tables. 5.6 Chapter Summary A database conceptual model was developed using the ER data modeling method. The model was derived from the analysis of survey of Division I Research Universities’ energy management systems and application of business information process analysis techniques. Integration of various parameters that affect energy consumption is vital for developing an energy management program, and the ER model concept provides a template for information system modeling in the relational database environment. In the next chapter, two case studies will be described to evaluate the feasibility of the conceptual model in the current institutional environment. 80 Chapter 6 Developing Proof of Concept In this chapter, case studies are discussed to evaluate the conceptual database model. Current energy management systems for two Division I Research Universities analyzed, and recommendations and changes were suggested for improving energy management processes in the case study universities. 6.1 Methodology Two case studies were done to evaluate the conceptual integrated energy management database developed in this research. They were focused on collecting information on the potential feasibility and adaptability of the conceptual integrated energy management model as a whole, and in their current university settings. Present energy management approaches and modes of operation adopted by the two case study universities were studied in detail. The first case study included interviews of university energy administrators. A validation packet including thesis abstract, simplified integrated energy management conceptual model, and consent forms were sent to the interviewees by email prior to the interview. The overall conceptual ER model was simplified by hiding the attributes and color shading and grouping the entities that belonged to a particular department. This was done to make the model more 81 comprehensible and to facilitate retrieving of the lnfonnation required from energy administrators. During the interview, the author explained the process and methods that were used to develop the model to the interviewee and collected their feedback. For the second case study, a validation packet including thesis abstract, the simplified integrated energy management conceptual model, and consent form were sent to the energy administrator by email. The energy administrator was asked to go through the validation packet and send their feedback on the feasibility and adaptability of the model as a whole, and in their current university setting. The data collected from the case studies was compared to the proposed integrated energy management conceptual model developed in this research. Based on the comparison, the researcher suggested recommendations and changes for improving some of the energy management processes in the case study universities. 6.2 Case Study 1 This case study involved an interview with an energy administrator, and collection of feedback on the potential feasibility of the conceptual database model. The data collected on various aspects of energy management is discussed in following sections. 6.2.1 Institution Overview As a Division I Research Institution, this university has a broad mission of teaching, research, extension, and outreach. Current enrollment is at 82 approximately 43,000 students. Academic buildings therefore serve a variety of faculty, staff, undergraduate, and graduate students engaged in a myriad of activities. Campus electrical usage has been growing at approximately 3% per year due to increases in the use of equipment such as computers and the addition of new buildings. Because of the size of the university, campus academic buildings are impacted by variety of administrative units. Classroom scheduling is conducted through the Scheduling Office within the Office of the Registrar. The Physical Plant oversees architectural and engineering services, custodial services, maintenance, utilities, recycling, and waste management, as well as other functions. Space is assigned to academic units by the office of Facilities, Planning, and Space Management. 6.2.2 Campus Physical Characteristics In this university, there are 626 buildings with 20.8 million gross square feet, and a replacement value of $2.1 billion. These buildings are spread over a campus in excess of 5,000 acres. Steam and electricity are delivered to campus buildings from the on-campus cogeneration power plant. The cogeneration plant is primarily a steam generation plant and electricity is a byproduct. Steam is used for space and water heating and for running chillers during the summer. The Architectural and Engineering Services housed in the campus physical plant keeps records of building construction details. The construction details are stored 83 as blueprints and CADD files. The university is in process of converting the blueprints to CADD files. 6.2.3 Energy Metering and Monitoring Process Energy metering is done for individual buildings by metering the monthly electricity and steam consumption. Electric meters are located in the mechanical rooms of the buildings. The readings are read manually every month and are entered in a spreadsheet. Steam metering methods are complex and vary with buildings. The method utilized in most buildings is by measuring the run time of the condensate pump, and then a conversion factor is applied for calculating BTU’s of steam consumed. The steam meters are also read manually every month. Energy monitoring at this university is done using Digital Direct Control (DDC) systems. A DDC uses a digital microprocessor to automatically control and monitor the operation of building heating, ventilation, and air conditioning (HVAC) systems and equipment. The energy monitoring room located in the campus physical plant tracks the HVAC equipment for all the buildings in the university. The equipment schedule is based on the room classroom schedule in that building. The classroom scheduling data is obtained from the Registrar’s Office at the start of every semester and is entered manually in a spreadsheet, which is then used for developing the equipment schedule. In some of the newer 84 buildings, the energy meters are also connected to the energy monitoring system. 6.2.4 Space Management Process The Office of Facilities Planning and Space Management maintain an inventory of space for all the university buildings. The two main components of the space database are rooms and buildings. Each building typically contains numerous rooms used for a variety of purposes. Most of the detailed data on how space is used, by whom, for what purposes, and other important variables are linked to the inventory of rooms (or assignable space). All rooms are allocated to one of the room use categories and each room belongs to an institutional unit that has the ownership of the space. The general fund classrooms in a building are managed by the Scheduling Office within the Registrar’s Office, which conducts classroom scheduling process for the campus. The classroom operation schedule is based on the course scheduled in that classroom. 6.2.5 Feedback from Energy Administrator The concept of using an integrated energy management database was considered useful by the energy administrator of the first case study university. The energy administrator acknowledged the idea of using a database to record and retrieve lnfonnation, and considered it an efficient way to organize large amounts of data. The administrator also acknowledged the benefits that the 85 integrated energy management database can bring to the university’s energy management system. According to the energy administrator, ownership of the database and the amount of initial setup required are some of the issues that need a little more attention before implementing the database. The energy administrator also pointed out the need for training on using database software and the requirements of easy to use DBMS software. 6.3 Case Study 2 This case study involved an email survey to a representative Division I Research University energy administrator, and collection of feedback on the potential feasibility of the conceptual database model. The data collected on various aspects of energy management is discussed in following sections. 6.3.1 Institution Overview As a Division I Research Institution, this university serves multiple missions including teaching, research, extension, and outreach. Academic buildings serve a variety of purposes including faculty and staff offices, graduate student labs and offices, administrative offices, classrooms and research labs. Steam and electricity are delivered to campus buildings by utility companies and the campus power plant. For the last five years campus energy usage has been reduced at approximately 6% per year due to various energy management efforts the 86 university has implemented. Some of the energy management efforts taken were technical retrofits including lighting retrofits, chilled water looping systems, insulation, HVAC controls, and preventive maintenance of HVAC systems The Facilities Management department oversees the maintenance, utilities, and engineering services on the campus. Campus space management is done by the Office of Space Management and the Office of the Registrar conducts the classroom scheduling process for the campus buildings. 6.3.2 Campus Physical Characteristics The Facilities Management department manages 146 campus buildings, and the annual energy consumption last year for the university was approximately 763 billion BTU’s. Steam and electricity are delivered to campus buildings from utility companies and the campus power plant. The facilities management department keeps records of building construction details, which are stored as blueprints. The Facilities Management department was recently recognized for outstanding progress in the implementation of its energy conservation program. Among the conservation measures cited were lighting measures including electronic ballasts and T8 Tri-phosphor lamps to replace older and less efficient fluorescent lamps, motion sensors, LED Exit signs, and the replacement of incandescent lamps with compact fluorescent lamps. The department is also working to improve the efficiency of the cooling and heating systems in university buildings, as the 87 energy consumed by these systems is the largest portion of the university’s energy bill. 6.3.3 Energy Metering and Monitoring Process Energy metering is done for individual buildings by metering the monthly electricity and steam consumption. KWH meters are used for metering electricity consumption, whereas steam metering is done by measuring the condensate. Computerized energy management systems, and meter readings are used for monitoring campus energy consumption. The university has developed a database to integrate energy monitoring and consumption data, which stores information on steam production and fuel consumption. 6.3.4 Space Management Process The Office of Space Management maintains an inventory of space for all campus buildings. The two main components of the space database are rooms and buildings. Each building typically contains numerous rooms used for a variety of purposes. All rooms are allocated to one of the room use categories and each room belongs to an institutional unit that has the ownership of the space. The classrooms in a building are managed by the Registrar's Office, which conducts the classroom scheduling process for the campus. 88 6.3.5 Feedback from Energy Administrator The energy administrator thought that the concept of using an integrated energy management database was helpful for better energy management in universities. The energy administrator recognized the benefits of the integrated energy management database. He also suggested that there is an obvious benefit in terms of consolidation of information, all of which relate to the same group of buildings. The energy administrator suggested that as maintenance is one of the primary functions of campus physical plant, an analysis should be done to check the benefits for integrating maintenance functions within the database. The energy administrator also pointed out that an analysis of implementation costs and training costs should be made to ascertain the level of implementation feasibility of the database. 6.4 Researcher's Recommendations and Changes The previous sections described and analyzed the energy management system used by the case study universities. In this section, some possible changes and benefits by applying the conceptual database model developed in Chapter 5 are discussed. The two main changes proposed are as follows 1. Use of a database to organize, store, and manage energy-related data, which are now stored in spreadsheets or drawings. The database can be built based 89 on the conceptual database model of this research. There is a need to develop individual databases to store information on energy metering and monitoring, building equipment inventory, campus physical characteristics, and building construction details. The database development process should not be cumbersome as most of the information on the systems, besides construction details, is stored in spreadsheets and just needs to be transferred from one format to another. 2. Integration of individual databases to develop an integrated energy management database. The integrated energy management database will extract energy-related information from the entire individual databases and store data in one centralized location. The database can be built based on the conceptual database model of this research. Figure 6.1 shows the integrated energy management conceptual model as applied for Case Study 1. 6.5 Benefits from the Proposed Changes The following benefits are observed from the changes proposed in section 6.4: 1. Benefit in achieving energy efficiency: The information stored and retrieved from an integrated energy management database will help in achieving energy efficiency in campus buildings by identifying the problem areas and suggesting solutions. The integration of classroom schedules with equipment schedules can lower the equipment running time and hence energy consumption. The integration of space details with the equipment data can help in achieving better space utilization so that same space type are 90 connected to an equipment. This will reduce the maximum energy demand and equipment running time. . Benefit in report generation: When energy related data is stored in a spreadsheet, energy administrators are required to go over all the data periodically to draw necessary information and reports. lf energy data are organized in a database, these periodical reports can be generated automatically by preset queries. The impact of certain areas such as student credit hours, space scheduling, and space types on building energy consumption can be found by implementing the integrated energy management database. The integrated energy management database can also help in developing benchmark data that can be used to assess and compare the energy efficiency of different campus buildings. Energy administrators can organize and manipulate data by using SQL queries and report utility to produce the desired information in different reports. . Benefit for future planning: After the energy related data are stored in a database, necessary data for future planning can be derived from queries. Data is stored in an easy to use format instead of hiding in different spreadsheet files. 6.6 Chapter Summary The case studies were focused on collecting information on the potential feasibility and adaptability of the conceptual integrated energy management model as a whole, and in the current university settings of the case study 91 universities. The current energy management systems of the case study universities were studied possible changes and recommendations based on the conceptual model were proposed. The conceptual database model and the benefits from implementing the model were discussed with the energy administrators and their feedback on the model was collected. The modifications and changes suggested by the energy administrators are reported in the feedback sections of the chapter, and are incorporated in the area of future research for this research work. The next chapter will discuss the future areas of research. 92 Chapter 7 Conclusions and Area of Future Research 7.1 Introduction This research was focused on developing a conceptual database model for integrated energy management in institutional buildings. The specific objectives were to analyze the existing energy management and organizational structure in representative Division-l Research Universities, to identify parameters and information required to be incorporated in an integrated energy management database, and to develop a conceptual data model for design of an integrated energy management database. This chapter discusses the conclusions that were drawn based on the research objectives. The area of future research describes how this research work can be used to further develop the conceptual model created in this research work. 7.2 Conclusions Based on the research work it can be concluded that the proposed integrated energy management conceptual model can be used in real life university settings. The conceptual model can be used to develop an integrated energy management database. This database system integrates pertinent energy related information form various administrative units and helps energy administrators in decision making, energy reporting, and achieving energy 93 efficiency in research universities. The conclusions related to the objectives of this research work are discussed below. 7.2.1 Conclusions about Energy Management and Organization Setup in Division I Research Universities The campus Physical Plant or the Facilities Management department is mainly responsible for energy management in research universities. Energy consumption has been decreasing during the last five years in universities that have an organized energy management plans or reduction targets in place. For universities with no formal or organized energy management plan, energy consumption has been increasing, because energy management was not one of the high priorities for the university. Due to the enormous size of Division I Research Universities, different departments within the universities store data on energy generation, consumption, electricity and steam metering, equipment scheduling, energy monitoring, university space inventory, and scheduling of classes. There is a huge amount of data stored in independent files or databases in each of these departments. lnfonnation flow between various energy-related departments is inefficient and limited. 94 7.2.2 Conclusions about Data Requirements in an integrated Energy Management Database Based on the survey of Division I Research Universities and case studies it can be concluded that data items to be included in the integrated energy management database are a function of size of the university, ease in availability of data, and cost-benefit factors associated with the data. Energy consumption data is one of the important database contents, as most universities have a system in place for monitoring energy consumption, and is a performance indicator for determining the benefits from energy efficiency efforts. Energy metering, energy monitoring, mechanical equipment, space scheduling and space utilization data were found to be essential in an integrated energy management database, and should be included based on the current energy management structure of the universities. It can further be concluded that inclusion of data on building construction details is useful but not mandatory and is a function of cost associated with including it, as most of the building construction details are not stored in electronic formats. 7.2.3 Conclusions about Conceptual Database Model Development Conceptual modeling is one of the critical steps in database design as it gives a clear understanding of the organization’s objectives and its functional areas, and is independent of software or hardware. In this research, ER modeling was used as the conceptual database-modeling tool. ER modeling is best suited for data driven databases. The data driven design approach is focused on the kinds of 95 data that are stored in the database and the interrelationships and constraints among various data. Whereas DFD (data flow diagram) is more suited for process driven database design. Process driven design of a database is more focused on how the activities or processes are performed, concentrating more on applications rather than data. The integrated energy management database model developed in this research consolidates energy-related information stored in different academic departments, and is flexible in nature. The database model can be easily modified depending upon the future needs of the energy management. 7.3 Limitations of the Research The main limitations to this research are as follows: 1. The integrated energy management database model is based on the data collected from energy administrators of seven (7) Division I Research Universities. The model assumes that the data collected is characteristic to a typical Division I Research University. 2. The integrated energy management database model includes energy parameters that had been identified by researchers in the field of energy management, the author’s experience, and data collected from the survey of Division I Research Universities. Other parameters that may be beneficial for increasing energy efficiency in an institutional building, but are not supported by existing research are not included in the model. 96 3. The model is designed based on the requirements and existing campus conditions in Division I Research Universities. With some modifications, the model may be applied to other university types. The feasibility of the model in other university types depends on the size of the university and the number of buildings managed by the campus physical plant. 7.4 Area of Future Research The research project was focused on developing a conceptual database model for integrated energy management in institutional buildings. The domain of the research work is institutional buildings' in Division I Research Universities. The conceptual model developed was based on ER modeling, initially used for accounting information systems design. The conceptual model was used to develop database tables in the integrated energy management database. Case studies of two universities were done to develop the proof of concept of the conceptual database model. Based on the research work and feedback from the case study universities, different areas of future research are identified. They are broadly classified as energy management database modeling, integration of facilities and energy management, and energy management software development. 7.4.1 Energy Management Database Modeling The domain for the proposed conceptual model for design of an integrated energy management database was institutional building. The same concept can 97 be applied to other building types such as residential, office, and healthcare facilities. As each of these building types have different energy parameters associated with them, research needs to be done to identify energy parameters and integrate those parameters in a database. 7.4.2 Integration of Facilities and Energy Management Facilities management is a key responsibility of campus physical plants or facilities management departments. The conceptual model developed considered only energy related lnfonnation stored in different departments within a university. As the information comes from different departments there is an issue of ownership and maintenance of the database. Integration of overall campus maintenance, energy generation or distribution details, and other physical plant activities in the integrated energy management database will help in achieving total integration and will give the ownership of the database to the Physical Plant. 7.4.3 Energy Management Software Development Energy efficiency in any facility can be achieved through retrofits and effective management of information and resources. Software is available in the market that helps to identify technical retrofits, calculating building energy losses, and monitoring energy consumption. This thesis sets fonrvard a framework for development of energy management software to integrate energy efficiency achieved through retrofits and information management. 98 APPENDICES 99 APPENDIX A FICM Data Properties‘ Outline of Building and Room Data Elements Table below lists the recommended and optional items of lnfonnation for each building and room in an inventory. These data elements are briefly outlined in this chapter. The technical definitions and coding structure are provided in Chapter 4 for buildings and Chapter 5 for rooms. This list is intended to indicate and provide guidance on which data elements are generally viewed as most important and useful for institutional management or external reporting. Neither category should be viewed as prescriptive, and institutions typically vary in which data elements are included in their facilities inventory. WW Recommended Data Elements Optional Data Elements Building Information Institutional identifier Location or Street Address Site Identifier Local Name Building Identifier Number of Floors Ownership Status Type of Construction Estimated Replacement Cost Landmark Status Year of Construction Original Building Cost Year of Beneficial occupancy Cost of Latest Major Renovation Year of Latest Major Renovation Fixed Equipment Disabled Access to Building Building Service Area Building Condition Circulation Area Gross Area Mechanical Area Assignable Area Structural Area Room lnfonnation Institutional Identifier Local Room Name Building Identifier Suitability Unique Space or Room Identifier Room Architectural Features Organizational Unit Room Fixed Equipment Assignable Area Room Moveable Equipment Room Use Category Academic Discipline Functional Use Number of Stations Disabled Access to Room ‘ Post Secondary Education Facilities Inventory 8. Classification Manual; National Center for Educational Statistics, US. Department of Education/Office of Educational Research and Improvement. November 1992. 100 Definitions of Space Use National Center for Education Statistics1 Classrooms: general-purpose classrooms, lecture halls, recitation rooms, seminar rooms, and other rooms used primarily for scheduled non-laboratory instruction. Also includes specialized instructional storage. Laboratory Facilities: Class Laboratory—rooms characterized by special purpose equipment or a specific configuration that ties instruction to a particular discipline or closely related group of disciplines Laboratory Facilities: Research— rooms characterized by special purpose equipment or a specific configuration that ties research activities to a particular discipline or closely related group of disciplines. Includes laboratory support such as wann/cold rooms, darkrooms etc. Office Facilities: Offices and conference rooms specifically assigned to each of the various academic, administrative, and service functions. Study Facilities: Study rooms, stacks, open-stack reading rooms, and library processing rooms. Special Use Facilities: Military training rooms, athletic and physical education spaces, media promotion rooms, clinics, demonstration areas, field buildings, animal quarters, greenhouses and other room categories which are sufficiently specialized in their primary activity of function to merit a unique room code. General Use Facilities: Assembly rooms, exhibition space, food facilities, lounges, merchandising facilities, recreational facilities, meeting rooms, child and adult care rooms and other facilities that are characterized by a broader availability to faculty, students, staff or the public than are special use areas Support Facilities: Computing facilities, shops, central storage areas, vehicle storage areas and central service space that provide centralized support for the activities of the campus. Healthcare Facilities: Facilities to provide patient care (human and animal) Residential Facilities: Housing facilities for students, faculty, staff, and visitors to the campus. Unclassified Facilities: Inactive or unfinished areas, or areas in the process of conversion. ‘ Post Secondary Education Facilities Inventory & Classification Manual; National Center for Educational Statistics, US. Department of Education/Office of Educational Research and Improvement. November 1992. 101 0.3.0001 ”G 39.296 0.3 3.: o. 8:8 8:20. 8.880 «2.8:. 8:330 .82 28.8.9.8 898.28. 8.88: .88.: 9: 2:8: .530 3 8:38 8.82.2. .235 223.53 8.322 0.688,. .3 85.0”. .80 m... 3:25.80 .88.: s: 298.392... 9: .50 8.3.0 8888:. N: E95288 528 628an 8.83:2 20.53%: m: 5353.: m... .3500 5 36:50 t. 5.50 ..:8 80.28 .553. :.< :32 28:82 8.33 8.5.58. 5.: 9.5:: 82.983... em._..s< 3.82.". I. .880 o... :85 0.33. we: 3.86:: .082: .85. caucuses. :s 50.2.85. :0 8:830 0.2.05 n: 8.29.0 .888... n. £2,550.00. coca—.2502 305.3 .0200 a «.0096 02.800000 in 320.0. .299. a... 8:390 8.59:8 8.8880 0: 8.86:0 80.50 58:3. 8.2.3 8:883 m... .553 t: :60 0.588.. 3 322550 a: 86880 60:25 N. 8.880 8......: I ”8.50 .8250 80.80 28.28 .::z and .85.... 03.8.80 2:0 3.8.30 2 8.5.8.. a 3.80 N: «.8: :m m: «2.890 5.88m 8.8:. 2.22552. .522 0...: N... .:..::<.:oo 0: £63. 8:900 28 5.8: N: 8.0280 :2 8.62.2. 22.880 3.5328”. N: .:.:.:< :0 8...... N: «8.38 36:82... N: 2:0 2.:0 8.. 25:83 528:8. 2. 8.5.2200 2. 63.8.95: E €2.86 ..0 E820 ..m 3:89.. 3 ..n. .095 ..n 08.2.8. ..N :00 .. 000.....«320 2.10.5030“. 0285.03 .239 $0550 50......» 00.3.0» >¢s...x:< 3.3053020» :25: .95.: 120....ng .2003» 0.3093 030:: .8309. 30.52.52. :... 3. :g :.: :6 :6 :.n :.N :0 gzoEEzgmo 20.5.0300 532000050: _ mzogozau no >SOZOx Implementation System —N Administration I Determine problems & opportunities New requirements Feasibility Study Existing system evaluation Functional requirements Data design Process design Interface design Database development Hardware, software installation Programming, coding & debugging Documentation & end user training Systems monitoring Administration and modifications Figure 0.1.1 The Information System Development Life Cycle Source: Modified from Coronel, 1997 Appendix E Database Design Methodologies Database development process is a subset of an overall Information system (IS) design process. Researchers have been working on the process of designing databases that are much faster and support multi tasks. It is been noticed that there is a need to design and use conceptual models in the early stages of database design methodology. E.1.1 Database Design Methodology There are mainly two kinds of database design methodology: process driven design and data driven design (Maciaszek, 1990; Raymond, 1987; Nijssen, 1989). These two methodologies also reflect the design philosophy of information system design. E.1.1.1 Process Driven Design Process driven design of a database is more focused on how the activities or processes are performed, concentrating more on applications rather than data. The initial design in this case starts with processes and derives initial database structures from the processes. The business functions or processes are first illustrated in a specified format. The direction of data flow, which can be from one 125 entity to other entities or vice versa, is established. Data storage locations and user interface criterion are also considered. A data flow diagram (DFD) is usually used as a graphic diagramming tool for representation of the logical data flow in a system. DFDs are drawn in an increasing level of detail, starting with summary level view and going down to more detailed levels for each summary level. Data dictionary is created that stores elements from the data flow and data storage locations. The preliminary database design is arrived from data flow and data storage in the flow. E.1.1.2 Data Driven Design The data driven design approach is focused on what kinds of data are stored in the database and the interrelationships and constraints among various data. The data driven approach builds the data stmcture based on general semantics of information used in business processes. The design starts with the determination of data structures that satisfies required properties and refines such structures once the processes are specified. The main task is to build a data representation model of the objective system, focusing on data and its properties. Once the data are identified and structured in a particular format, data storage and networking procedure are elaborated for proper design of the database. Finally, data access and processing, and user interface is designed based on current information system (IS) objectives. Entity relationship (ER) modeling is a widely accepted technique for the design of conceptual database models, which is also used in this research. The database modeling and design for integrated energy 126 management data model is considered to be a data-driven approach as the design starts with the determination of data requirements, and is concerned about the data content rather than process. The data driven approach is also chosen in this research, because data can be represented in a more generic model without specifying the particular processes. 127 Appendix F Database Design Process There are several methodologies for database design, but for research purposes database design process can be summarized in four distinct steps: a) lnfonnation requirement analysis b) conceptual database design 0) logical design, and d) physical design (McCarthy, 1994; Rob & Coronel, 1997). Although all the four steps can be applied in a generic way for this research, but the main focus will be on information requirement study and conceptual database design. The logical and physical design of the integrated energy management database is outside the scope of this research. Information Requirement Analysis Information requirement analysis can be simply defined as a procedure that attempts to determine the present and future information needs of the user community (McCarthy, et. al., 1994). Because database design is normally carried out within a functioning organization, information requirement analysis is usually conducted by examining what is already going on within the organization’s information system and what are the future needs. The existing information system is analyzed to find the end user data view and identify data and their characteristics. In this research, information on the existing energy management system in Division I research universities is collected by a 128 structured survey of energy administrators of Division I research universities. The survey is then analyzed to define function and operation rules, characterize and classify data items, and data requirements of the integrated energy management database model. Conceptual Design The purpose of conceptual design is to describe the information content of the database rather than storage structures that will be required to manage this information (Batini, et. al., 1992). In this step, the designer uses the previously accumulated knowledge about information requirements to produce a conceptual design of the database. When a data model describes a set of concepts from a given reality, it is a conceptual data model. When used in database design, it is called a conceptual database model. This model gives a clear understanding of the organization’s objectives and its functional areas and is independent of software or hardware. Conceptual database model design is accomplished by creating a graphic representation using conceptual modeling techniques, usually entity relationship (ER) diagrams for data models. Compared with other modeling techniques, ER model is the most popular and standard data model used for conceptual database design. Its ability to incorporate semantics and simplicity in communication between designer and end user makes ER the best choice. The most common approach to conceptual database design in context of ER model is called view synthesis. It begins from the users’ perspective; an ER model is 129 prepared for each user view that the database is to support. Then these diagrams are combined in a relatively mechanical way to produce a unified ER model capable of supporting all of the views. At this point, the complete ER model can be drawn and reviewed for completeness and consistency. In this research, ER modeling technique will be used to develop an integrated energy management conceptual database model. Independent ER models will be created for each user view, and then the different models will be integrated in a single ER model encompassing all the user views. In a large database design, where more than one function area are involved multiple views of relationships and data result. Integration is a concept to consolidate these views into a single global view to eliminate redundancy and inconsistency from the model. Logical Design Logical design includes activities that convert a DBMS independent conceptual model into the internal model for a selected DBMS such as ORACLE, MS Access, DBASE, and SQL SERVER. For the relational DBMS, the logical design includes design of tables, views and access rights. The logical design translates the software independent conceptual model into a software dependent model by defining the appropriate domain definitions, the required table structure, and necessary access restrictions (Rob 8. Coronel, 1997). This research is focused on conceptual design and hence this phase is not included in the scope of the research. 130 Physical Design The physical model is a framework of the database to be stored on physical devices. The physical design is the process of selecting data storage and networking characteristics of a database. It decides the location of the data in storage devices and system performance. There are several steps involved in physical design including selection of platform, time-space tradeoff, application programming for data retrieval, user interface design, and characteristics of direct access devices. The physical design is very technical job, and is not only software dependent but also depends on the hardware. As the focus of this research is to develop a conceptual model for integrated energy management database, this phase is beyond the scope of this research. 131 Appendix 6 Database Design Strategies There are two classical approaches to database design: 1. Top—down design: It starts by identifying the data sets, and then defines the data elements for each of those data sets. This process involves the identification of different entity types and the definition of each entity’s attribute. 2. Bottom-up design: It starts by identification of data elements (items), and then grouping them together in data sets. In other words, first define attributes and then group them to form entities. The selection of a primary emphasis on top-down or bottom-up procedures often depends on the scope of the problem. Although these two methodologies are complimentary rather than mutually exclusive, a primary emphasis on bottom up approach may be more productive for small databases for few entities, attributes, and relationships. For situations in which the number, variety and complexity of entities, attributes, and relationships is overwhelming, a primarily top-down approach is more easy to use. In this research, primarily a top-down approach is used for conceptual model design, where the data sets were identified first and then the data elements. 132 Database Design Philosophies Depending on the scope and size of the system, organization’s management style, or the organization structure the database design may be based on two very different design philosophies a) Centralized design or b) Decentralized design (Rob & Corolnel, 1997) Centralized Design is productive when the data component is composed of a relatively smaller number of objects and procedures. Centralized design is typical of relatively simple and/or small databases and can be carried out and represented in a simple database. In the centralized design option, a single conceptual design is completed and then validated. Figure 6.1.1 summarizes the centralized design option. ———> Conceptual Model Design Conceptual Model Verification Data Dictionary Figure 6.1.1 Centralized Design Adopted from Rob and Coronel (1997) 133 Decentralized approach is used when the data component of the system has a considerable number of entities and complex relationships. Decentralized design is also likely to be employed when the problem itself is spread across several operational areas and each element is a subset of entire set. Within the decentralized design framework, the database design task is divided into several modules. A conceptual model is developed for each module and verified. After the verification process has been completed, all modules are integrated in one conceptual model. Figure 6.1.2 summarizes the decentralized design option. Data Component Submodule Criteria Conceptual Subset A Subset B Subset C Models . . View process View process View process Verification Constraints Constraints Constraints Aggregation Data Dictionary Figure 6.1.2 Decentralized Centralized Design Adopted from Rob and Coronel (1997) 134 Appendix H Data Table Properties Table Name Column Data Type Campus Power Plant Financial year Date/Time _P_oyver Plgnt NaLn_e, Text Plant Steam Capacity Number Plant Electricity Capacity Number Actual Electricity Produced Number Actual Steam Produced Number Plant Type Text Plant Description Text Unit Steam generation Cost Currency Unit electricity Generation Cost Currency Plant Efficiency Number Location Text Utility Company Financial year Date/Time Utility Company Name Text Location Text Actual Steam Purchased Number Actual Electricity Purchased Number 135 Energy Monltoring system Electricity Meters Electricity Meter Reading Steam Meters Steam Meter Reading Unit Steam purchasing Cost Unit electricity purchasing Cost Monitoring Equipment ID Equipment Type Description Location Electricity Meter IQ Building ID Building Name Monitoring equipment ID Description Location Electricig Meter ID Date read Meter reading Conversion Factor (if any) BTU Equivalent Steam Meter ID Building ID Building Name Monitoring equipment ID Description Location Steam Meter ID Date read Meter reading 136 Currency Currency Number Text Text Text Number Number Text Number Text Text Number Date/Time Number Number Number Number Number Text Text Text Text Number Date/Time Number Building Equipment Equipment Category Equipment Schedule Building Conversion Factor (if any) BTU Equivalent Eguipment ID Equipment Category ID Building ID Building Name Monitoring Equipment ID Description Type Capacity Energy Consumption rate Eguipment Categogy ID Equipment Category Description Eguipment lD Semester Date Start time End Time Building ID Building Name Financial Year Power Plant Name Utility Company Name Building Functional Use Code Year of Construction Gross Area Assignable Area 137 Number Number Number Number Number Text Number Text Text Number Number Number Text Number Text Date/Time Date/Time Date/Time Number Text Date/1' ime Text Text Number Date/Time Number Number Building Functional Use Maintenance Log Research Functional Use Room Total Cubic feet Location Building Owner Type of constmction Building Functional Use Code Building Functional Use Description Maintenance Log # Description Date Comments Research Functional Use Code Description Yearly Research Dollars Spent Yearly Research Man-Hours mm Room Number Building ID Building Name Room Functional Use Code Institutional Unit ID Room Use Category Code Room SF Area Capacity Floor Lighting Load Office Equipment Lead 138 Number Text Text Text Number Text Number Text Date/Time Text Number Text Currency Number Number Number Number Text Number Number Number Number Number Number Number Number Room Use Category Room Use Category ID Number Rom Use Category description Text Room Functional Use Room Functional Use Code Number Room Functional Use Text Code Description Institutional Unit Institutional Unit ID Number Institutional Unit Description Text Classrooms Room ID Number Room Number Number Room Capacity Number Room SF Area Number Lighting load Number Office Equipment Load Number Course Course ID Number Room ID Number Room Number Number Department Text Student enrollment for the Number scheduled course Course Schedule Course ID Number Semester Number Date Date/Time Start Time Date/Time End Time Date/Time Construction Assemblies Assembly ID Number 139 Walls Fenestration Basement Slabs Roof Building ID Building Name Description Assembly IQ Average “U” Value for Wall Year of Construction Gross Area Wall Description Assembly ID Average “U” Value for Fenestration Year of Construction Gross Area Fenestration Description Assembl ID Average “U” Value for basement Slab Year of Construction Gross Area Basement Slab Description Assembly ID Average “U” Value for Roof Year of Construction Gross Area Roof Description 140 Number Text Text Text Number Date/Time Number Text Number Number Date/Time Number Text Number Number Datefl’ime Number Text Number Number Date/Time Number Text APPENDIX J Standard Queries Q. 1 Rooms in a building served by particular building equipment This query will give a list of rooms in building 123 that are connected to the equipment with equipment ID as 234. Field Buildan ID Room ID Table Buildiryq Room Criterion Buildinng= 123 Equipment ID=234 0.2 Steam consumption for the year 1998, in buildings that are primarily used for instructional use. Field Building ID Steam Meter Reading Meter Meter ID Readigq Table Building Steam Steam Meter Steam Meter Meters Reading Reading Criterion Where Building When Date= When Date= Functional Use 01/01/98 01/01I99 Code=1 (Code for instructional spaces) 0.3 List of Campus buildings that are served by utility companies Field Building ID Table Building Criterion Utility Company ¢ Null Value 141 Q.4 Average envelope heat loss through building 123 Field Assembly ID Average Average “U” Average Average “U” value value for “U” value “U” value for walls fenestration for roof for Basement Slab Table Construction Walls Fenestration Roof Basement Assemblies Slab Criterion Where Building ID=123 Field Heat loss through Average “U” Average “U” Average walls value for value for roof “U” value fenestration for Basement Slab Table Query 4 Query 4 Query 4 Query 4 Criterion Average U value* Average U Average U Average U A*At value* A*At value* A*At value* A*At 05 Lighting Load for building 123 Field Room ID Lighting Load Table Building Room Criterion Building_lD= 123 142 LIST OF REFERENCES (APPA 1994) The Association of Higher Education Facilities Officers (APPA) “The Energy Management Workbook”1994 (Burns 1999) Burns, M. ‘Developing a Corporate Energy program” World Workplace Conference proceeding, pp31-37, October 1999 (Carnegie Classification 1994) Carnegie Classification of Institutions of Higher Education “http://www.camegiefoundation/OurWorlchassiflcation/CIHE94” 1 994 (CRC handbook 1997) Kreith, F. & West, R.E “CRC Handbook of Energy Efficiech pp. 7-9, 1997 (Ferriera, 1999) Ferriera, Al. “Energy Deregulation Update: Facility Cost Reduction Opportunities” World Work Place Conference Proceedings, Volume-I, 1999. (FICM, 1992) “Facilities lnfonnation and Classification Manual" 1992 (Krieth and West 1997) Kreith, F. & West, R.E “CRO Handbook of Energy Efficiency". 1997 (Kanzelberger, 1990) Kanzelberger, Jeffery W. “Holistic Energy Management” Critical Issues in Facilities Management- Energy Management, Volume-6. Published by Association of Physical Plant Administrators of Universities and Colleges. 1990 (Karkia, 1990) Karkia, Reza M. “ How to Structure an Energy Management and Conservation Program”. Critical Issues in Facilities Management- Energy 143 Management, Volume-6. Published by Association of Physical Plant Administrators of Universities and Colleges. 1990 (Lennig, 1995) Lennig, Richard 0. “Load Management” IEEE Recommended Practice for Energy Management in Industrial and Commercial Facilities. IEEE Std 739-1995 (Maciaszek, 1980) Maciaszek, L.A, “Database Design and Implementation” 1980 (Nawab 1994) Nawab, M. " Integrated Energy Planning for Intuitional Buildings” 1994 (NCES 1994) US National Center for Education Statistics Data, NCES "http://www.nces.ed.gov" 1994 (Nijssen, 1989) Nijssen, G.M., Halpin, T.A. “Conceptual Schema and Relational Databases Design”, Prentice Hall, 1989 (O’Brien, 1994) O’Brien, James A. “Intmduction to lnfonnation Systems” Richard D. Irwin, Inc, 1994 (Purinton & Swistock, 1990) Purinton, Brad and Swistock, John R. “Utility Infrastructure Development at a Major Research University". Critical Issues in Facilities Management- Energy Management, Volume-6. Published by Association of Physical Plant Administrators of Universities and Colleges. 1990 (Qayoumi, 1999) Qayoumi, Mohammed H. “ Utilities Metering and Measurement” Facilities Manager March/April 1999, Article-2. 1999 144 (Raymond, 1987) Raymond, Louis. “lnfonnation Systems Design for Project Management: A Data Modeling Approach." Project Management Journal, Volume XVIII, Number4, P94-99. 1987 (Rob 8. Coronel, 1997) Rob, Peter and Coronel, Carlos. “Database Systems- design, implementation, management” Course Tchnology 1997 (Rush & Johnson 1989) Rush, S.C & Johnson, S.L. “ The Decaying American Campus: A ticking Time Bomb” pp. 67-69, 1989 (Smith1990) Smith, C.H. “ The Second Coming of Energy Conservation.” Unite Reader, The Best Of Alternative Press, No. 37, pp. 114-115, January/February 1990 (T alonpoika, et al, 1995) Talonpoika, Raine, Karstila, Lassila, Kenneth, Pallas, and Jukka COMBINE, (1995) “Computer Model for the Building Industry in Europe”. [Online] Available http://erg.ucd.ie/combine.html (Teorey, 1994) Teorey, Tobey. J, “Database Modeling and Design: The Fundamental Principles”, Morgan Kaufmann Publishers, Inc 1994 (Tucker 1988) Tucker, R.A. “ A practical approach to office lighting renovation.” Energy Technology Conference, W.DC. February 17-19, 1988 (Verderber 8. Siminovitch 1989) Verderber, R. & Siminovitch, M. “ Retrofitting: Sure it Saves Money, But Does It Work?” Electrical System Design, Vol.69, No.3, April 1989 145 IIIIIIIIIIIIIIIIIIIIIIIIIIIIIII IIIIIIllllllljlllljllllllllljljllllllll