Thesis Abstract

Abstract
This thesis explores the application of machine learning techniques in predicting building energy consumption, with the aim of improving energy efficiency and sustainability in the built environment. The increasing demand for energy in buildings poses significant challenges in terms of energy management and conservation. Traditional methods of energy consumption prediction often fall short in accuracy and efficiency, thus necessitating the utilization of advanced machine learning algorithms to enhance predictive capabilities. The research begins with a comprehensive introduction highlighting the background of the study, problem statement, objectives, limitations, scope, significance, and the structure of the thesis. The literature review in Chapter Two provides an in-depth analysis of existing studies, theories, and technologies related to building energy consumption prediction using machine learning approaches. This chapter examines key concepts such as regression analysis, neural networks, support vector machines, decision trees, and ensemble learning techniques in the context of energy forecasting. Chapter Three details the research methodology employed in this study, including data collection methods, feature selection, model development, training, and evaluation techniques. The methodology section outlines the steps taken to preprocess the data, select appropriate machine learning algorithms, and validate the predictive models to ensure their accuracy and reliability. In Chapter Four, the findings of the research are presented and discussed in detail. The results of the machine learning models in predicting building energy consumption are analyzed, compared, and interpreted to assess their effectiveness and performance. This chapter also addresses any challenges encountered during the research process and provides insights into potential future research directions. The conclusion and summary in Chapter Five encapsulate the key findings, contributions, implications, and recommendations derived from the study. The thesis concludes by emphasizing the importance of applying machine learning in predicting building energy consumption to achieve energy efficiency goals, reduce environmental impact, and promote sustainable practices in the built environment. In conclusion, this thesis contributes to the growing body of knowledge on the application of machine learning in predicting building energy consumption. By leveraging advanced algorithms and predictive models, this research aims to enhance energy management strategies, optimize resource allocation, and facilitate informed decision-making in the design and operation of energy-efficient buildings.

Thesis Overview