Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system

Due to the high energy consumption in buildings, cooling load forecasting plays a crucial role in the planning, control and operation of heating, ventilating and air-conditioning systems. However, cooling load data series always exhibit nonlinear and dynamic features, making it very difficult to be...

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Vydané v:	Energy (Oxford) Ročník 148; s. 269 - 282
Hlavný autor:	Fu, Guoyin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Oxford Elsevier Ltd 01.04.2018 Elsevier BV
Predmet:	Air conditioners Air conditioning Air-conditioning system algorithms Artificial intelligence Belief networks Buildings case studies China Cooling Cooling load prediction Cooling loads Cooling systems data collection Decomposition Deep belief network Deep learning Electricity consumption energy Energy consumption Ensemble technique Feature extraction Forecasting heat Machine learning Mathematical models model uncertainty Outliers (statistics) planning prediction Uncertainty China Deep learning Ensemble technique Air-conditioning system Deep belief network Cooling load prediction
ISSN:	0360-5442, 1873-6785
On-line prístup:	Získať plný text
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