TY  - BOOK
AU  - Magoul�es,F.
AU  - Zhao,Haixiang
TI  - Data mining and machine learning in building energy analysis
T2  - Computer engineering series
SN  - 9781118577592
AV  - QA76.9.D343 M34 2016eb
U1  - 006.312 23
PY  - 2016///
CY  - London, Hoboken, NJ
PB  - ISTE, Wiley
KW  - Data mining
KW  - Machine learning
KW  - Buildings
KW  - Energy conservation
KW  - Research
KW  - Mathematical models
KW  - Data Mining
KW  - Machine Learning
KW  - Constructions
KW  - �Economies d'�energie
KW  - Recherche
KW  - Exploration de donn�ees (Informatique)
KW  - Apprentissage automatique
KW  - Mod�eles math�ematiques
KW  - COMPUTERS
KW  - General
KW  - bisacsh
KW  - fast
N1  - Includes bibliographical references and index
N2  - "The energy performance in buildings is influenced by many factors, such as ambient weather conditions, building structure and characteristics, occupants and their behaviors, operation of sublevel components like heating, ventilation and air-conditioning systems. These complex properties make the prediction, analysis or fault detection/diagnosis of building energy consumption very difficult to perform accurately. This book focuses on up-to-date data mining and machine-learning methods to solve these problems."--Preface; "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."--Provided by publisher
UR  - https://onlinelibrary.wiley.com/doi/book/10.1002/9781118577691
ER  -