Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms

Predicting next-day heat load curves is essential to guarantee sufficient heat supply and optimal operation of district heat systems (DHSs). Existing studies have mainly investigated one-step ahead forecasting methods, which can predict a single value at a future time step. To predict heat load curv...

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Vydáno v:	Energy (Oxford) Ročník 188; s. 116085
Hlavní autoři:	Xue, Puning, Jiang, Yi, Zhou, Zhigang, Chen, Xin, Fang, Xiumu, Liu, Jing
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Elsevier Ltd 01.12.2019 Elsevier BV
Témata:	Algorithms Artificial intelligence Artificial neural networks Bluegrass music case studies China Coefficient of variation Direct strategy District heating Forecasting heat Heat load forecasting Heating systems Learning algorithms Machine learning Machine learning algorithms Mathematical models Model accuracy Modelling Multi-step ahead forecasting Neural networks Performance assessment prediction Predictions Recursive functions Recursive methods Recursive strategy regression analysis Strategy Support vector machines China Multi-step ahead forecasting Machine learning algorithms Heat load forecasting District heating Recursive strategy Direct strategy
ISSN:	0360-5442, 1873-6785
On-line přístup:	Získat plný text
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