Smart Building Automation Systems for Energy Optimization and User Comfort

Thesis Abstract

The rapid urbanization and increasing energy demands in contemporary building environments necessitate the development of intelligent systems that balance energy efficiency with occupant comfort. This study addresses the critical gap in integrated control mechanisms for smart building automation by proposing an advanced, ICT-driven system aimed at optimizing energy consumption while ensuring enhanced user comfort. The primary aim is to design, implement, and evaluate a comprehensive smart automation framework that leverages sensor data, machine learning algorithms, and user feedback to dynamically adjust building operations. Specific objectives include developing a multi-sensor data acquisition system, constructing predictive models for energy optimization, analyzing user comfort levels, and assessing system performance in real-world scenarios. The research adopts a mixed-methods approach, combining quantitative experimental validation with qualitative user surveys, to comprehensively evaluate system effectiveness. The quantitative component involves deploying the automation system within a case study building—an office complex comprising 50 offices in a metropolitan setting—for a period of six months. Data collection instruments include wireless sensor networks capturing indoor environmental parameters (temperature, humidity, lighting, occupancy), energy consumption meters, and a structured user feedback questionnaire. The sample size involves 200 occupants distributed across different zones within the building, with stratified random sampling ensuring representative demographics. Qualitative data are analyzed through thematic analysis, while quantitative data undergo statistical analysis through regression techniques and ANOVA to identify significant factors influencing energy savings and user satisfaction. The anticipated findings suggest that the integrated automation system can achieve up to 25% reduction in energy use compared to baseline consumption, with a corresponding increase in occupant comfort levels as measured by standardized scales. The predictive models based on machine learning, such as multiple regression and decision trees, are expected to accurately forecast environmental conditions and optimize control strategies in real time. Additionally, the study hypothesizes that user feedback correlates positively with system adaptability, reinforcing the system’s capacity to tailor environmental settings to individual preferences without compromising energy efficiency. The findings will demonstrate the effectiveness of context-aware control algorithms, supporting the theoretical framework grounded in the Theory of Planned Behavior and the Ecological Systems Theory, which posit that behavior change towards sustainable practices can be facilitated through technological and environmental modifications. This research significantly extends existing knowledge by providing empirical evidence on the integration of sensor networks, machine learning, and user-centric design principles within smart building systems, which has been underexplored in current literature. It offers a pragmatic framework for deploying adaptive automation solutions in commercial buildings, emphasizing scalability and contextual relevance. The study contributes to the body of knowledge on ICT-driven interventions for sustainable urban development and enhances understanding of user-system interactions in intelligently controlled environments. In conclusion, the study asserts that sophisticated, data-driven automation systems can substantially improve energy efficiency without diminishing occupant comfort, thereby aligning environmental sustainability with user-centered design. It recommends the adoption of such systems in commercial real estate portfolios, along with continuous system refinement based on occupant feedback and environmental data. Future research should explore long-term impacts on occupant health and productivity, as well as the integration of renewable energy sources into building automation protocols, to further advance sustainable building practices at broader scales.