Smart Building Energy Management Systems Using IoT and AI Integration

Thesis Abstract

The increasing energy consumption in modern buildings, driven by growing urbanization and technological advancements, necessitates innovative solutions to optimize energy efficiency while maintaining occupant comfort. Traditional building management systems often lack the responsiveness and adaptability required to effectively monitor and control energy use in complex, dynamic environments. This study aims to develop and evaluate an integrated smart building energy management system (BEMS) that leverages Internet of Things (IoT) sensors and artificial intelligence (AI) algorithms to optimize energy consumption. Specifically, the research seeks to identify the most effective IoT sensor array configurations, develop machine learning models for predictive energy demand and occupancy patterns, and assess system performance in real-time operational settings. The research adopts a mixed-methods approach, combining quantitative experimental design with qualitative case study analysis. The population comprises 50 commercial office buildings within the metropolitan area, with a stratified random sample of 15 buildings selected based on size, occupancy type, and technological readiness. Data collection instruments include a network of IoT sensors installed to continuously monitor parameters such as temperature, humidity, light levels, occupancy, and energy consumption over a six-month period. Additionally, structured interviews with facility managers and building occupants are conducted to gather qualitative insights into system usability and perceived benefits. Quantitative data are analyzed primarily through multivariate regression analysis to establish relationships between sensor inputs and energy consumption, while machine learning techniques, including random forest and neural networks, are employed to develop predictive models. Qualitative data are thematically analyzed to explore user acceptance, system integration challenges, and operational barriers. The key findings are expected to demonstrate that the integration of IoT and AI significantly reduces energy consumption—estimated at 20-30% savings based on preliminary modeling—by enabling precise demand response, adaptive control, and predictive maintenance. The findings are anticipated to identify optimal sensor configurations and highlight the effectiveness of AI-driven decision-making in dynamic building environments. The research will also reveal critical factors influencing system adoption and operational success, including technological interoperability, user training, and maintenance protocols. This study contributes to the existing body of knowledge by providing empirical evidence on the feasibility and impact of IoT-AI integrated systems in building energy management, offering a comprehensive framework for system design, deployment, and evaluation. It advances understanding of how intelligent automation can be tailored to diverse building types and climatic conditions, and it highlights the potential for scalable, cost-effective solutions in the building industry. The research also extends theoretical insights into the application of the Technology Acceptance Model (TAM) and Diffusion of Innovations (DOI) theory within the context of smart building technologies. The main conclusion underscores the transformative potential of IoT and AI integration in achieving sustainable, energy-efficient building operations. Recommendations emphasize the importance of developing standardized protocols for sensor deployment, investing in user training programs, and fostering policy environments that encourage technological innovation. The study advocates for further longitudinal research to assess long-term system impacts, scalability studies across different geographic regions, and the integration of renewable energy sources within smart building frameworks to enhance sustainability goals. Overall, this research offers a significant step toward realizing intelligent, responsive, and energy-conscious building environments through advanced ICT-enabled solutions.