Occupant-Centric Energy Management: Balancing Privacy, Well-being, and Sustainability in Smart Buildings

Greenhouse gases trap heat near earth's surface, causing warming. Unlike solar warming, which heats the entire atmosphere, greenhouse gases primarily warm the lower atmosphere while cooling the upper layers. Since the industrial revolution, human activities, primarily burning fossil fuels for energy, have significantly increased atmospheric $CO_2$ levels. This increase is the main driver of recent climate change. The global context of rising temperatures necessitates sustainable energy solutions. This thesis reveals critical limitations within current energy management systems, particularly in multi-occupancy buildings. A key research problem is the lack of integrated systems that effectively balance occupant well-being with energy efficiency and privacy. This thesis investigates the development and evaluation of Multimodel Energy Management Systems (MEnMS) for smart buildings, addressing the critical challenges of energy optimization, occupant comfort through personalization, privacy, and sustainability. The research addresses four primary challenges: dynamic spatio-temporal variation in personalized comfort, privacy concerns in IoT-enabled systems, real-time translation of energy consumption to carbon emissions, and the design of a sustainable EnMS adaptable for multiple stakeholders. An IoT-enabled, location-aware smart energy management system is designed and developed, demonstrating significant energy savings and enhanced occupant comfort through the optimization of dynamic lighting configuration. A method combining federated learning with a two-state Markov model is implemented to ensure privacy in personalized EnMS, achieving high accuracy in appliance state prediction while preserving data confidentiality. An integrated MEnMS utilizing IoT architecture is proposed, achieving an average 25% of energy savings through hierarchical control and real-time data analysis for the lighting system. The EnSAF framework, built using the SAF Toolkit, is introduced, providing a structured approach to design sustainability-aware EnMS by addressing diverse stakeholder concerns and utilizing a Decision Map and Software Quality Model. The research contributions include the development of algorithms for occupant localization and appliance preference prediction, the design of an integrated energy management framework, and the creation of a sustainability assessment framework. The implementation of energy management systems in buildings is crucial to reduce energy consumption and carbon emissions. EnMS offers economic and environmental benefits by optimizing energy use. The achievement of energy sustainability requires the collaboration of all stakeholders to build resilient and sustainable communities. The thesis proposes and evaluates several innovative solutions. The thesis concludes by revisiting the research questions, summarizing the findings, and suggesting future directions, including the integration of HVAC systems, the expansion of appliance state prediction algorithms, the analysis of IoT infrastructure overhead, and the refinement of the EnSAF framework for enhanced usability and carbon neutrality.