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Introduction

In recent years, the concept of smart buildings has gained significant momentum due to advancements in technology and the growing demand for sustainable energy management practices. Smart buildings are equipped with various sensors and devices that collect data on energy consumption, occupancy patterns, and environmental conditions, enabling building managers to optimize energy usage and improve the overall efficiency of the building.

One key technology that is revolutionizing smart building energy management is Edge AI. Edge AI refers to the deployment of artificial intelligence algorithms on edge devices such as sensors, controllers, and gateways, allowing for real-time data processing and decision-making at the edge of the network. By leveraging Edge AI, smart buildings can analyze large volumes of data locally, reducing latency and bandwidth requirements, while also enhancing privacy and security.

This thesis aims to explore the potential of Edge AI for smart building energy management by investigating its capabilities, limitations, and implementation challenges. The study will focus on developing AI-based algorithms and frameworks that can optimize energy usage, improve building sustainability, and enhance user comfort in smart buildings.

Table of Contents

Chapter 1: Introduction
1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of Smart Building Energy Management
2.2 Artificial Intelligence in Smart Buildings
2.3 Edge Computing Technologies
2.4 Edge AI Applications in Energy Management
2.5 Challenges and Opportunities of Edge AI in Smart Buildings
2.6 Case Studies of Edge AI Implementation
2.7 Best Practices for Edge AI Integration
2.8 Regulatory and Ethical Considerations
2.9 Future Trends in Edge AI for Smart Buildings
2.10 Summary of Literature Review

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 AI Algorithm Development
3.5 Simulation and Testing Procedures
3.6 Evaluation Metrics
3.7 Ethical Considerations
3.8 Limitations of Research Methodology

Chapter 4: Discussion of Findings
4.1 Data Analysis and Results
4.2 AI Algorithm Performance Evaluation
4.3 Comparison with Existing Solutions
4.4 Implications for Smart Building Energy Management
4.5 Recommendations for Implementation
4.6 Future Research Directions

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Conclusion
5.3 Contributions of the Study
5.4 Practical Implications
5.5 Limitations and Future Research Recommendations

Thesis Overview: Edge AI for Smart Building Energy Management

The integration of Edge AI in smart building energy management systems presents a promising opportunity for improving energy efficiency, sustainability, and user comfort. This thesis will investigate the potential of Edge AI in optimizing energy usage through real-time data analytics and decision-making at the edge of the network. By developing AI-based algorithms and frameworks, this study aims to address the challenges and opportunities of implementing Edge AI in smart buildings, providing valuable insights for building managers, researchers, and policymakers in the field of sustainable energy management.

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