Data Mining and Machine Learning in Building Energy Analysis Towards High Performance Computing

Magoules, Frederic/ Zhao, Hai-xiang

ISBN 10: 1848214227 / ISBN 13: 9781848214224

Published by Iste/Hermes Science Pub, 2016

About this Item

164 pages. 9.50x6.50x0.50 inches. In Stock. This item is printed on demand.

Seller Inventory # __1848214227

Bibliographic Details

• Title: Data Mining and Machine Learning in Building Energy Analysis Towards High Performance Computing
• Publisher: Iste/Hermes Science Pub
• Publication Date: 2016
• Language: English
• ISBN 10: 1848214227
• ISBN 13: 9781848214224
• Binding: Hardcover
• Condition: Brand New

About this title

Synopsis

The energy consumption of a building has, in recent years, become a determining factor during its design and construction. With carbon footprints being a growing issue, it is important that buildings be optimized for energy conservation and CO2 reduction. This book therefore presents AI models and optimization techniques related to this application.

The authors start with a review of recent models for the prediction of building energy consumption: engineering methods, statistical methods, artificial intelligence methods, ANNs and SVMs in particular. The book then focuses on SVMs, by first applying them to building energy consumption, then presenting the principles and various extensions, and SVR. The authors then move on to RDP, which they use to determine building energy faults through simulation experiments before presenting SVR model reduction methods and the benefits of parallel computing. The book then closes by presenting some of the current research and advancements in the field.

About the Author

Frédéric Magoulès is Professor at the Ecole Centrale Paris in France and Honorary Professor at the University of Pècs in Hungary. His research focuses on parallel computing, numerical linear algebra and machine learning.

Hai-Xiang Zhao is Senior Researcher at Amadeus in France. His research focuses on parallel computing, data mining and machine learning.