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Introduction

In recent years, with the rapid advancement of technology, there has been a growing interest in the use of artificial intelligence (AI) to improve the management of smart buildings. Smart buildings are equipped with a variety of sensors and devices that collect data on various aspects of the building’s environment, such as temperature, occupancy, and energy usage. This data can be used to optimize building operations, reduce energy consumption, and improve occupant comfort. One of the challenges in smart building management is the sheer volume of data that is generated by these sensors and devices. Traditional cloud-based AI systems may not be able to process this data quickly enough to make real-time decisions. This is where edge AI comes in.

Edge AI refers to the use of AI algorithms and models that are deployed on edge computing devices, such as sensors, gateways, and controllers, rather than in a centralized cloud server. By processing data closer to where it is generated, edge AI systems can reduce latency, improve efficiency, and enhance privacy and security. In the context of smart building management, edge AI can enable real-time decision-making, predictive maintenance, and energy optimization.

This thesis aims to explore the use of edge AI for smart building management. We will investigate how edge AI can be used to process data from sensors and devices in a smart building environment, and how it can be integrated into existing building management systems. The goal is to develop a system that can optimize building operations, reduce energy consumption, and improve occupant comfort in real-time.

Table of Contents
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

2. Literature Review
2.1 Overview of Smart Building Management
2.2 AI and Machine Learning in Smart Buildings
2.3 Edge Computing in Smart Buildings
2.4 Edge AI Applications in Building Management
2.5 Challenges and Opportunities of Edge AI in Smart Buildings

3. System Design and Methodology
3.1 System Architecture
3.2 Data Collection and Preprocessing
3.3 Edge AI Model Selection
3.4 Integration with Building Management Systems
3.5 Testing and Evaluation
3.6 Performance Metrics
3.7 Security and Privacy Considerations
3.8 Scalability and Flexibility

4. System Implementation
4.1 Hardware and Software Requirements
4.2 Data Acquisition and Integration
4.3 Edge AI Model Development
4.4 Real-Time Data Processing
4.5 Energy Optimization Algorithms
4.6 User Interface Design
4.7 Deployment and Testing
4.8 Performance Analysis

5. Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Future Work
5.4 Concluding Remarks

Thesis Overview on Edge AI for Smart Building Management

Edge AI has the potential to revolutionize smart building management by enabling real-time decision-making, predictive maintenance, and energy optimization. This thesis explores the use of edge AI in smart buildings and aims to develop a system that can optimize building operations, reduce energy consumption, and improve occupant comfort. Through a comprehensive literature review, system design and methodology, system implementation, and conclusion and summary, this thesis will provide insights into the challenges and opportunities of using edge AI in smart building management. The findings of this research will not only contribute to the field of smart building management but also pave the way for future research in this exciting and emerging area of study.

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