Multi-Energy Load Forecasting in Integrated Energy System Based on ResNet-LSTM Network and Attention Mechanism
Wang Chen1, Wang Ying1, Zheng Tao2,3, Dai Zemei2,3, Zhang Kaifeng1
1. Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing 210096 China; 2. NARI Group (State Grid Electric Power Research Institute) Co. Ltd Nanjing 211106 China; 3. NARI Technology Development Co. Ltd Nanjing 211106 China
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.
王琛, 王颖, 郑涛, 戴则梅, 张凯锋. 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[J]. 电工技术学报, 2022, 37(7): 1789-1799.
Wang Chen, Wang Ying, Zheng Tao, Dai Zemei, Zhang Kaifeng. Multi-Energy Load Forecasting in Integrated Energy System Based on ResNet-LSTM Network and Attention Mechanism. Transactions of China Electrotechnical Society, 2022, 37(7): 1789-1799.
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2022, Vol. 37 
