Load forecasting of district heating system based on Informer

Accurate load forecasting of district heating systems (DHSs) is an essential guide to guaranteeing effective energy production, distribution, and rational utilization. Artificial neural networks have been extensively applied to heating energy prediction in DHS. Recently, a new time series prediction...

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Podrobná bibliografie
Vydáno v:	Energy (Oxford) Ročník 253; s. 124179
Hlavní autoři:	Gong, Mingju, Zhao, Yin, Sun, Jiawang, Han, Cuitian, Sun, Guannan, Yan, Bo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Elsevier Ltd 15.08.2022 Elsevier BV
Témata:	Air quality Air temperature Algorithms Artificial neural networks Autoregressive models China District heating District heating system energy Energy distribution Forecasting heat Heating Heating load Heating load forecasting Heating systems Informer Long short-term memory Multilayer perceptrons Neural networks Outdoor air quality Performance assessment prediction Prediction models Recurrent neural networks Relative humidity Relative position encoding algorithm System effectiveness temperature time series analysis Wind speed China Heating load forecasting District heating system Informer Relative position encoding algorithm
ISSN:	0360-5442, 1873-6785
On-line přístup:	Získat plný text
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