基于ResNet-LSTM网络和注意力机制的综合能源系统多元负荷预测

doi:10.19595/j.cnki.1000-6753.tces.210212

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Abstract In an integrated energy system, different types of loads, i.e. electrical loads, heat loads, cooling loads, might have complex and strong coupling relationships among them. Compared with forecasting each type of load separately, to forecast multi-energy loads together in a combined multi-task model can further explore the internal connections and therefore improve the accuracy of forecasting. A multi-task learning model based on ResNet-LSTM network and Attention mechanism is proposed to fit the spatial coupling relationship and time coupling relationship between multi-energy loads. Firstly, the multi-layer ResNet is used as the feature extraction unit of the multi-energy load data to mine the spatial coupling interaction characteristics between the multi-energy; secondly, the LSTM residual structure is used to further mine the time series characteristics of the multi-energy loads data; then, the Attention mechanism is used to realize that multiple subtasks have different degrees of attention to shared features, which reflects the differentiated selection of shared features by different subtasks, and realizes joint forecasting of multiple loads. Finally, we applied the proposed method with the data at the Campus Metabolism system of Arizona State University. Compared with other forecasting models, the results show that the proposed method has higher accuracy.

Key words： Attention mechanism ResNet long short-term memory(LSTM) multi-energy load forecasting multi-task learning

Received: 19 February 2021

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TM715

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	Wang Chen
	Wang Ying
	Zheng Tao
	Dai Zemei
	Zhang Kaifeng

Cite this article:

Wang Chen,Wang Ying,Zheng Tao等. Multi-Energy Load Forecasting in Integrated Energy System Based on ResNet-LSTM Network and Attention Mechanism[J]. Transactions of China Electrotechnical Society, 2022, 37(7): 1789-1799.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.210212 OR https://dgjsxb.ces-transaction.com/EN/Y2022/V37/I7/1789

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