Data Mining and Machine Learning in Building Energy Analysis

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John Wiley & Sons, Feb 8, 2016 - Computers - 186 pages
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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Data Acquisition for Building
Artificial Intelligence Models
Artificial Intelligence for Building
Model Reduction for Support
Parallel Computing for Support
Summary and Future of Building Energy Analysis

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About the author (2016)

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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