Smart Building Energy Management through IoT-Integrated Adaptive Control Systems

Thesis Abstract

The rapid escalation of energy consumption in modern buildings and the urgent need for sustainable management strategies have underscored the importance of integrating innovative technologies such as the Internet of Things (IoT) to optimize energy efficiency. This study addresses the persistent challenge of achieving adaptive and responsive energy management in smart buildings through IoT-enabled control systems. The primary aim is to design, implement, and evaluate an IoT-integrated adaptive control framework that dynamically modulates building energy systems to enhance efficiency, reduce operational costs, and improve occupant comfort. Specific objectives include examining the current limitations of traditional building management systems, developing an integrated IoT-based control model informed by the Behavior-Based Theories of Human-Technology Interaction and Control Theory, and empirically assessing its performance within a real-world building environment. The research adopts a mixed-methods approach, combining quantitative and qualitative data collection techniques to garner comprehensive insights. The quantitative component involves deploying the IoT-enabled adaptive control system in a medium-sized commercial office complex comprising 150 office occupants over a period of six months. A sample of 200 data points is collected through IoT sensors that monitor variables such as temperature, humidity, occupancy, lighting, and energy consumption. Data collection instruments include IoT sensor networks, control system logs, and energy consumption meters. For qualitative insights, semi-structured interviews are conducted with building occupants and facility managers to elicit perceptions of system usability, occupant comfort, and behavioral responses. Quantitative data are analyzed using multiple regression analysis to evaluate the relationship between system interventions and energy savings, while time-series analysis assesses trends in energy consumption patterns over the intervention period. Thematic analysis of interview transcripts identifies key themes related to user acceptance, system reliability, and perceived benefits of the IoT-integrated system. The study incorporates a comparative analysis with conventional building management systems to determine relative performance improvements. Expected findings suggest that the IoT-embedded adaptive control system will significantly reduce energy consumption by 25-30% compared to baseline measures, while maintaining or enhancing occupant comfort levels. The analysis is anticipated to reveal that adaptive, real-time adjustments facilitated by IoT sensors lead to optimized HVAC operation, lighting control, and occupancy patterns, driven by the control model based on Control Theory. Additionally, occupants are likely to express increased satisfaction with environmental conditions, and facility managers will report improved operational efficiency and system responsiveness. This investigation contributes to the existing body of knowledge by proposing a comprehensive, scalable framework for IoT-based adaptive energy management systems in buildings, grounded in established behavioral and control theories. The study highlights the potential of IoT integration to transform traditional building management into a proactive, intelligent system capable of autonomous decision-making. It further identifies critical success factors and barriers associated with system deployment, including technological reliability, data security, and user acceptance. The study concludes that IoT-integrated adaptive control systems present a viable and effective solution for sustainable energy management in smarter buildings. Recommendations include adopting standardized protocols for IoT sensor deployment, enhancing occupant engagement strategies, and integrating machine learning algorithms for predictive analytics. Future research should explore long-term system maintenance, scalability across different building types, and integration with renewable energy sources. This research provides practical insights and a validated framework for architects, engineers, and policymakers aiming to promote energy-efficient building practices through advanced ICT solutions.