Data Mining and Machine Learning in Building Energy Analysis [Hardcover]

Focusing on up-to-date artificial intelligence models to solve building energy problems, Artificial Intelligence for Building Energy Analysis reviews recently developed models for solving these issues, including detailed and simplified engineering methods, statistical methods, and artificial intelligence methods. The text also simulates energy consumption profiles for single and multiple buildings. Based on these datasets, Support Vector Machine (SVM) models are trained and tested to do the prediction. Suitable for novice, intermediate, and advanced readers, this is a vital resource for building designers, engineers, and students.

Preface ix

Introduction  xi

Chapter 1. Overview of Building Energy Analysis 1

1.1. Introduction 1

1.2. Physical models 3

1.3. Gray models 6

1.4. Statistical models 6

1.5. Artificial intelligence models 8

1.5.1. Neural networks  8

1.5.2. Support vector machines 13

1.6. Comparison of existing models  14

1.7. Concluding remarks . 16

Chapter 2. Data Acquisition for Building Energy Analysis 17

2.1. Introduction  17

2.2. Surveys or questionnaires 18

2.3. Measurements 21

2.4. Simulation 25

2.4.1. Simulation software 26

2.4.2. Simulation process  28

2.5. Data uncertainty  34

2.6. Calibration 35

2.7. Concluding remarks  37

Chapter 3. Artificial Intelligence Models 39

3.1. Introduction  39

3.2. Artificial neural networks 40

3.2.1.lãJ