A review on the prediction of building energy consumption

A review on the prediction of building energy consumption. Zhao, H. & Magoulès, F. Renewable and Sustainable Energy Reviews, 16(6):3586–3592, August, 2012.

Paper doi abstract bibtex

The energy performance in buildings is influenced by many factors, such as ambient weather conditions, building structure and characteristics, the operation of sub-level components like lighting and HVAC systems, occupancy and their behavior. This complex situation makes it very difficult to accurately implement the prediction of building energy consumption. This paper reviews recently developed models for solving this problem, which include elaborate and simplified engineering methods, statistical methods and artificial intelligence methods. Previous research work concerning these models and relevant applications are introduced. Based on the analysis of previous work, further prospects are proposed for additional research reference.

@article{zhao_review_2012,
	title = {A review on the prediction of building energy consumption},
	volume = {16},
	issn = {1364-0321},
	url = {http://www.sciencedirect.com/science/article/pii/S1364032112001438},
	doi = {10.1016/j.rser.2012.02.049},
	abstract = {The energy performance in buildings is influenced by many factors, such as ambient weather conditions, building structure and characteristics, the operation of sub-level components like lighting and HVAC systems, occupancy and their behavior. This complex situation makes it very difficult to accurately implement the prediction of building energy consumption. This paper reviews recently developed models for solving this problem, which include elaborate and simplified engineering methods, statistical methods and artificial intelligence methods. Previous research work concerning these models and relevant applications are introduced. Based on the analysis of previous work, further prospects are proposed for additional research reference.},
	number = {6},
	urldate = {2018-11-09},
	journal = {Renewable and Sustainable Energy Reviews},
	author = {Zhao, Hai-xiang and Magoulès, Frédéric},
	month = aug,
	year = {2012},
	keywords = {Artificial intelligence, Building, Energy consumption, Engineering methods, Prediction, Statistical models},
	pages = {3586--3592},
}

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