Analysis and Optimization of Building Energy Consumption using Artificial Intelligence

Thesis Abstract

Abstract
The rapid increase in energy demand in buildings has led to a pressing need for effective energy management strategies. This thesis focuses on the analysis and optimization of building energy consumption using artificial intelligence techniques. The objective of this research is to develop a predictive model that leverages artificial intelligence algorithms to optimize energy consumption in buildings, thereby reducing energy costs and environmental impact. Chapter One provides an introduction to the study, including the background of the research, problem statement, objectives, study limitations, scope, significance, and the structure of the thesis. The chapter also defines key terms used in the research to ensure clarity and understanding. Chapter Two presents a comprehensive literature review that covers ten key areas related to building energy consumption, artificial intelligence applications in energy management, predictive modeling, optimization techniques, and relevant case studies. This review forms the theoretical foundation for the research study. Chapter Three outlines the research methodology, detailing the approach, data collection methods, selection of artificial intelligence algorithms, model development, and validation techniques. The chapter also discusses the evaluation criteria used to assess the performance of the predictive model in optimizing building energy consumption. Chapter Four presents the findings of the research study, including the analysis of energy consumption patterns, the effectiveness of the artificial intelligence model in predicting energy usage, and the optimization results achieved. The chapter discusses the implications of the findings and provides insights into potential improvements and future research directions. Chapter Five concludes the thesis by summarizing the key findings, highlighting the contributions to the field of energy management, discussing the practical implications of the research, and offering recommendations for further studies. The conclusion emphasizes the importance of leveraging artificial intelligence for sustainable building energy management and outlines potential applications in real-world scenarios. Overall, this thesis contributes to the growing body of knowledge on energy management in buildings by demonstrating the effectiveness of artificial intelligence techniques in optimizing energy consumption. The research findings have implications for energy efficiency, cost savings, and environmental sustainability, making a significant impact on the field of building energy management.