The demand for energy efficiency has increased significantly in response to global efforts to reduce carbon emissions and enhance sustainability. The building sector alone accounts for nearly 30% of global energy consumption, highlighting the need for building technologies such as sensors that support energy-efficiency, and a skilled workforce capable of designing, implementing, and managing energy-efficient building systems. Meeting these workforce demands requires a shift from traditional educational approaches to more dynamic, technology-driven training solutions. Immersive technologies, such as Virtual Reality (VR) and interactive virtual models, have emerged as powerful tools for transforming energy workforce training. In addition, the integration of advanced sensor systems such as occupancy sensors plays a critical role in optimizing building energy efficiency. By incorporating sensor systems into immersive training platforms, trainees can experiment with various control strategies and observe the immediate effects on energy consumption, thereby gaining practical insights into the benefits and limitations of these systems. Therefore, this dissertation is structured around two main focus areas: (1) the investigation of sensor systems, particularly occupancy counting sensors, to evaluate their performance and accuracy in optimizing building energy efficiency, and (2) the use of VR and interactive virtual models to enhance training in energy audits and complex building energy systems for workforce development.The integration of sensors in this research develops a standardized methodology for testing the reliability of occupancy counting sensors, including typical and failure testing phases. Typical testing assesses sensor performance under normal conditions, while failure testing identifies performance limits under extreme scenarios. A case study involving two novel sensor systems in office and academic building settings demonstrates the methodology with a modified confusion matrix to effectively identify failures. Findings suggest that the testing methodology enables a more reliable evaluation of occupancy counting sensors, supporting their integration into energy management systems for improved building efficiency. The second focus area explores the use of VR and interactive virtual models to enhance energy audit training and workforce development, comparing their effectiveness in improving participants' ability to identify energy-saving opportunities and understand complex building systems. Results indicate that virtual training methods outperform traditional approaches, by improving knowledge gain in HVAC system training and enhancing participants' ability to identify energy-saving opportunities. The study further highlights the potential of VR technology and interactive virtual models to bridge the gap between theoretical knowledge and practical application, offering scalable training solutions for energy professionals. Overall, this dissertation contributes to advancing energy efficiency and workforce development by addressing critical gaps in both sensor technology and training methodologies. Together, these contributions support the transition to a more sustainable and energy-efficient built environment, equipping the workforce with the skills needed to meet the challenges of a rapidly evolving energy area.
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