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Synopsis
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.
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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.
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- PublisherWiley-ISTE
- Publication date2016
- ISBN 10 1848214227
- ISBN 13 9781848214224
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages186
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Data Mining and Machine Learning in Building Energy Analysis
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Data Mining and Machine Learning in Building Energy Analysis (Computer Engineering)
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Data Mining and Machine Learning in Building Energy Analysis
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ISTE Ltd and John Wiley and Sons Inc, 2016
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Data Mining and Machine Learning in Building Energy Analysis
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Data Mining and Machine Learning in Building Energy Analysis
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Data Mining and Machine Learning in Building Energy Analysis
Published by
ISTE Ltd and John Wiley & Sons Inc, 2016
ISBN 10: 1848214227
ISBN 13: 9781848214224
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Data Mining and Machine Learning in Building Energy Analysis (Hardcover)
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ISTE Ltd and John Wiley & Sons Inc, London, 2016
ISBN 10: 1848214227
ISBN 13: 9781848214224
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Hardcover. Condition: new. Hardcover. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781848214224
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ARTIFICIAL INTELLIGENCE FOR BUILDING EN
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Gebunden. Condition: New. 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,. Seller Inventory # 35208858
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Data Mining and Machine Learning in Building Energy Analysis (Iste)
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