Developing window behavior models for residential buildings using XGBoost algorithm

•Longitudinal behavioral data were collected from six apartments, lasting for 136 days.•Window behavior models were developed for residential buildings in China.•XGBoost algorithm showed better prediction performance than logistic regression. Buildings account for over 32% of total society energy co...

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Bibliographic Details
Published in:	Energy and buildings Vol. 205; p. 109564
Main Authors:	Mo, Hao, Sun, Hejiang, Liu, Junjie, Wei, Shen
Format:	Journal Article
Language:	English
Published:	Lausanne Elsevier B.V 15.12.2019 Elsevier BV
Subjects:	Accuracy Air conditioners Air conditioning Algorithms Artificial intelligence Behavior Behavior modeling Building design Buildings Computer simulation Energy consumption Energy efficiency Learning algorithms Logistics regression Machine learning Mathematical models Model accuracy Regression analysis Residential areas Residential buildings Ventilation Window behavior XGBoost algorithm Window behavior XGBoost algorithm Behavior modeling Logistics regression Residential buildings
ISSN:	0378-7788, 1872-6178
Online Access:	Get full text
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