Thesis Abstract

Abstract
The increasing demand for energy efficiency and sustainability in residential buildings has prompted the development of innovative solutions for effective energy management. This thesis presents the design and implementation of an Intelligent Home Energy Management System (I-HEMS) aimed at optimizing energy consumption in households. The system integrates advanced technologies such as Internet of Things (IoT), machine learning algorithms, and data analytics to provide real-time monitoring, control, and optimization of energy usage within a smart home environment. Chapter 1 of the thesis provides an introduction to the research topic, outlining the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter 2 presents a comprehensive literature review covering ten key aspects related to home energy management systems, IoT technologies, machine learning algorithms, and energy optimization strategies. In Chapter 3, the research methodology is detailed, including the system architecture design, hardware and software components, data collection methods, algorithm selection, simulation techniques, and evaluation criteria. The chapter also discusses the implementation process, testing procedures, and validation of the I-HEMS prototype in a real-world setting. Chapter 4 delves into a thorough discussion of the findings obtained from the implementation and testing of the I-HEMS system. This includes the analysis of energy consumption patterns, performance evaluation of the system in optimizing energy usage, user feedback, and comparison with existing energy management solutions. Finally, Chapter 5 presents the conclusion and summary of the thesis, highlighting the key insights, contributions, limitations, and future research directions. The I-HEMS system demonstrates significant potential in enhancing energy efficiency, reducing electricity costs, and promoting sustainable practices in residential buildings. The integration of advanced technologies and intelligent algorithms enables the system to adapt to user preferences, predict energy demands, and optimize energy consumption in a dynamic and proactive manner. In conclusion, the Design and Implementation of an Intelligent Home Energy Management System for Optimal Energy Consumption offers a practical and innovative solution to address the challenges of energy management in smart homes. This research contributes to the advancement of smart grid technologies, sustainable living practices, and the realization of energy-efficient residential environments.

Thesis Overview