Smart IoT-enabled Building Management System for Energy Optimization

Thesis Abstract

The rapid proliferation of smart building technologies and the increasing demand for sustainable energy consumption necessitate innovative solutions for optimizing energy efficiency in modern structures. This study addresses the critical challenge of integrating Internet of Things (IoT) devices within building management systems (BMS) to enhance energy performance, reduce operational costs, and support environmental sustainability. The primary aim of the research is to develop, implement, and evaluate a comprehensive IoT-enabled Building Management System (BMS) that leverages real-time data analytics to optimize energy use in commercial buildings. Specific objectives include (1) identifying key parameters influencing energy consumption in smart buildings, (2) designing an IoT-based framework for real-time monitoring and control of building systems, (3) developing algorithms for predictive maintenance and adaptive energy optimization, and (4) assessing the system’s effectiveness in reducing energy costs and improving occupant comfort. The research adopts a mixed-methods approach, combining quantitative data analysis with qualitative assessments to provide a holistic understanding of the system's performance. The study population includes 20 commercial office buildings within a metropolitan city, selected through stratified random sampling to ensure representativeness across building types and sizes. A sample of 200 sensor nodes and 50 IoT-enabled control units were installed across these buildings to capture data on lighting, HVAC (heating, ventilation, and air conditioning), occupancy patterns, and energy consumption over a period of 12 months. Data collection instruments comprised IoT sensors, energy meters, and a bespoke data acquisition platform, complemented by semi-structured interviews with building managers to gather contextual insights. To ensure validity and reliability, calibration procedures for sensor devices were adhered to, and reliability testing of the data acquisition system yielded a Cronbach’s alpha coefficient of 0.85. Quantitative data were analyzed using regression analysis to determine the relationships between system variables and energy savings, while repeated measures ANOVA assessed temporal variations in energy consumption pre- and post-implementation. Thematic analysis of interview transcripts provided qualitative insights into user perception, system usability, and operational challenges. The analytical framework also incorporated the adoption of the Technology Acceptance Model (TAM) to evaluate user acceptance of the IoT-based management system and the application of machine learning algorithms, such as support vector machines and neural networks, for predictive energy demand modeling. Expected findings include significant reductions in overall energy consumption—anticipated at 20-30%—and improvements in occupant comfort levels, evidenced by decreased temperature variability and enhanced air quality, attributable to adaptive control mechanisms. The system's predictive analytics are expected to facilitate proactive maintenance, thereby decreasing downtime and operational costs. It is also projected that the study will demonstrate high levels of user acceptance and perceived usefulness, supporting broader adoption of IoT solutions in building management. This research contributes to the existing body of knowledge by providing empirical evidence of the efficacy of IoT-driven approaches to building energy management, highlighting the integration of data analytics with intelligent control systems within a practical framework. Furthermore, it extends the theoretical understanding of technology acceptance in the context of sustainable building innovations, aligning with the Diffusion of Innovations theory and TAM. The study concludes that IoT-enabled BMS has substantial potential to transform energy management practices in commercial buildings, promoting sustainability and operational efficiency. Recommendations include the adoption of standardized data protocols for IoT devices, policy incentives to encourage smart building upgrades, and further research into scalable cloud-based analytics platforms. Future studies are advised to explore long-term system durability, scalability across different building typologies, and integration with renewable energy sources to maximize environmental benefits.