Data Mining and Machine Learning in Building Energy Analysis - Rilegato

Magoules, Frederic; Zhao, Hai-xiang

Rilegato

ISBN 10: 1848214227 ISBN 13: 9781848214224

Casa editrice: Iste/Hermes Science Pub, 2016

Riassunto

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.

Su questo libro

Informazioni sull?autore

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.

Dettagli dell?edizione

• Editore: Iste/Hermes Science Pub
• Data di pubblicazione: 2016
• Lingua: Inglese
• ISBN 10: 1848214227
• ISBN 13: 9781848214224
• Rilegatura: Copertina rigida
• Numero di pagine: 164
• Contatto del produttore: non disponibile