Data mining and machine learning in building energy analysis /
Fr�ed�eric Magoul�es, Hai-Xiang Zhao.
- 1 online resource (xiv, 164 pages : illustrations
- Computer engineering series .
- Computer engineering series (London, England) .
Includes bibliographical references and index.
"The energy performance in buildings is influenced by many factors, such as ambient weather conditions, building structure and characteristics, occupants and their behaviors, operation of sublevel components like heating, ventilation and air-conditioning systems. These complex properties make the prediction, analysis or fault detection/diagnosis of building energy consumption very difficult to perform accurately. This book focuses on up-to-date data mining and machine-learning methods to solve these problems."--Preface "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."--Provided by publisher