Smart Building Energy Management Using IoT and AI Integration

Thesis Abstract

The escalating demand for sustainable building operations combined with the rapid advancement of Internet of Things (IoT) and Artificial Intelligence (AI) technologies underscores the critical need for intelligent energy management solutions in modern buildings. Traditional building energy systems often lack the adaptive capacity to efficiently respond to dynamic occupancy patterns and environmental conditions, leading to unnecessary energy consumption and elevated operational costs. This study aims to develop and evaluate a comprehensive IoT and AI-driven framework for optimizing energy consumption in smart buildings, thereby contributing to sustainability goals and operational efficiency. The specific objectives are to design an integrated sensor network for real-time data acquisition, develop machine learning algorithms for predictive analysis of energy loads, assess the impact of the proposed system on energy savings, and analyze user comfort levels alongside energy efficiency metrics. The research adopts a mixed-methods approach centered on a case study of a commercial office building located in a metropolitan setting, with an overall population comprising building management staff, occupants, and sensor systems. A purposive sample of 150 building occupants and five building management personnel will be selected to gather qualitative insights, while data from over 200 IoT sensors installed within the building will provide quantitative inputs. Data collection instruments include IoT sensors for environmental and occupancy data, semi-structured interview protocols for stakeholder perspectives, and survey questionnaires for occupant satisfaction. To ensure validity and reliability, the sensor calibration process will be rigorously conducted, pilot testing of survey instruments will be performed, and triangulation will be employed to corroborate qualitative and quantitative data. Ethical considerations, such as informed consent and data privacy, will be observed throughout the research process. Data analysis will be conducted using advanced analytical techniques. Quantitative data from sensors will be analyzed via time-series analysis, regression modeling, and machine learning algorithms—including Random Forest and Support Vector Machine (SVM)—to predict energy demand and optimize consumption patterns. Qualitative data from interviews and surveys will be subjected to thematic analysis. The study will also apply the Technology Acceptance Model (TAM) and Diffusion of Innovations Theory to evaluate user acceptance and adoption factors. A hybrid analytical framework combining predictive modeling and thematic analysis will enable a comprehensive understanding of system performance and user perspectives. The expected findings include a significant reduction in building energy consumption—projected at approximately 25-30% based on simulation models—attributable to adaptive control algorithms that preemptively adjust HVAC and lighting systems. The intelligent system is anticipated to improve occupant comfort levels, evidenced by increased occupant satisfaction scores by at least 15%. Results from the regression analysis are expected to demonstrate strong correlations between occupancy patterns and energy use, validating the efficacy of the AI predictive models. Furthermore, qualitative insights are anticipated to reveal high acceptance levels among occupants and management, influenced by perceived ease of use and perceived usefulness as outlined in TAM. This research contributes to the expanding body of knowledge on smart building management by integrating IoT sensors, AI predictive analytics, and user-centered design within a unified framework. It provides empirical evidence that such integration can yield substantial energy savings while maintaining or improving occupant comfort. The study also advances understanding of factors influencing technology acceptance in the context of intelligent building systems. Policy implications include recommendations for scalable deployment strategies, data privacy protocols, and sustainable operational practices for building stakeholders. In conclusion, the study underscores the transformative potential of IoT and AI technologies in enabling sustainable, efficient, and occupant-centered building environments. The recommendations advocate for broader adoption of intelligent energy management systems, emphasizing customization to contextual needs, stakeholder engagement, and continued innovation through emerging technologies. Future research directions include exploring integration with renewable energy sources and expanding such systems to different building typologies to generalize findings across diverse urban settings.