Research on ultra-short-term load forecasting method of oil and gas field integrated energy system based on hybrid neural network

Aiming at the source-network-load coordination requirements of distributed new energy and cooling, heating and electricity loads in oil and gas fields, this paper proposes an ultra-short-term load forecasting method based on Convolutional-Bidirectional Long Short Term Memory-Skip (CNN-BiLSTM-Skip)....

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Vydáno v:	Electrical engineering Ročník 107; číslo 10; s. 14021 - 14036
Hlavní autoři:	Zhang, Zhao, Dong, Dezhi, Lv, Lili, Peng, Liyuan, Li, Bing, Peng, Miao, Cheng, Tingting
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2025 Springer Nature B.V
Témata:	Accuracy Algorithms Alternative energy sources Artificial neural networks Back propagation networks Big Data Cooling Economics and Management Electric power systems Electric vehicles Electrical Engineering Electrical loads Electrical Machines and Networks Electricity Energy consumption Energy management Energy Policy Energy resources Energy storage Engineering Forecasting Heating Integrated energy systems Machine learning Natural gas Neural networks Optimization Original Paper Power Electronics Regression analysis Stability Support vector machines Time series Wavelet transforms Wind power China Prediction accuracy Comprehensive energy system CNN-BiLSTM-Skip Hybrid neural network Ultra short term load forecasting
ISSN:	0948-7921, 1432-0487
On-line přístup:	Získat plný text
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