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Nonlinear Theory of Elastic Plates

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Exterior Algebras

Elementary Tribute to Grassmann's Ideas

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From Pinch Methodology to Pinch-Exergy Integration of Flexible Systems

Thermodynamics Energy, Environment, Economy Set

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Data Treatment in Environmental Sciences

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Gas Hydrates 1

Fundamentals, Characterization and Modeling

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Smart Decisions in Complex Systems

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Chi-squared Goodness-of-fit Tests for Censored Data

Stochastic Models in Survival Analysis and Reliability Set Volume 3

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Challenges and Intricacies of Marketing in China

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Supply Chain Management and Business Performance

The VASC Model

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Asymmetric Alliances and Information Systems

Issues and Prospects

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Data Mining and Machine Learning in Building Energy Analysis

Frédéric Magoulès, Ecole Centrale Paris, France Hai-Xiang Zhao, Amadeus, France

ISBN: 9781848214224

Publication Date: January 2016   Hardback   186 pp.

110.00 USD

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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.


1. Overview of Building Energy Analysis.
2. Data Acquisition for Building Energy Analysis.
3. Artificial Intelligence Models.
4. Artificial Intelligence for Building Energy Analysis.
5. Model Reduction for Support Vector Machines.
6. Parallel Computing for Support Vector Machines.

About the Authors

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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