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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Vydané v:	Energy (Oxford) Ročník 253; s. 124179
Hlavní autori:	Gong, Mingju, Zhao, Yin, Sun, Jiawang, Han, Cuitian, Sun, Guannan, Yan, Bo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Oxford Elsevier Ltd 15.08.2022 Elsevier BV
Predmet:	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 prístup:	Získať plný text
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