Data Mining and Machine Learning in Building Energy Analysis

Data Mining and Machine Learning in Building Energy Analysis

by Frédéric Magoules, Hai-Xiang Zhao
Released February 2016
Publisher(s): Wiley-ISTE
ISBN: 9781848214224

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Book description

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.

Table of contents

  1. Cover
  2. Title
  3. Copyright
  4. Preface
  5. Introduction
  6. 1 Overview of Building Energy Analysis
    1. 1.1. Introduction
    2. 1.2. Physical models
    3. 1.3. Gray models
    4. 1.4. Statistical models
    5. 1.5. Artificial intelligence models
    6. 1.6. Comparison of existing models
    7. 1.7. Concluding remarks
  7. 2 Data Acquisition for Building Energy Analysis
    1. 2.1. Introduction
    2. 2.2. Surveys or questionnaires
    3. 2.3. Measurements
    4. 2.4. Simulation
    5. 2.5. Data uncertainty
    6. 2.6. Calibration
    7. 2.7. Concluding remarks
  8. 3 Artificial Intelligence Models
    1. 3.1. Introduction
    2. 3.2. Artificial neural networks
    3. 3.3. Support vector machines
    4. 3.4. Concluding remarks
  9. 4 Artificial Intelligence for Building Energy Analysis
    1. 4.1. Introduction
    2. 4.2. Support vector machines for building energy prediction
    3. 4.3. Neural networks for fault detection and diagnosis
    4. 4.4. Concluding remarks
  10. 5 Model Reduction for Support Vector Machines
    1. 5.1. Introduction
    2. 5.2. Overview of model reduction
    3. 5.3. Model reduction for energy consumption
    4. 5.4. Model reduction for single building energy
    5. 5.5. Model reduction for multiple buildings energy
    6. 5.6. Concluding remarks
  11. 6 Parallel Computing for Support Vector Machines
    1. 6.1. Introduction
    2. 6.2. Overview of parallel support vector machines
    3. 6.3. Parallel quadratic problem solver
    4. 6.4. MPI-based parallel support vector machines
    5. 6.5. MapReduce-based parallel support vector machines
    6. 6.6. MapReduce-based parallel support vector regression
    7. 6.7. Concluding remarks
  12. Summary and Future of Building Energy Analysis
  13. Bibliography
  14. Index
  15. End User License Agreement

Product information

  • Title: Data Mining and Machine Learning in Building Energy Analysis
  • Author(s): Frédéric Magoules, Hai-Xiang Zhao
  • Release date: February 2016
  • Publisher(s): Wiley-ISTE
  • ISBN: 9781848214224