Short-Term Load Forecasting Based on Multi-branch Residual Gated Convolution Neural Network

doi:10.11930/j.issn.1004-9649.202112004

Abstract

Abstract: Short-term load forecasting is one of the important tasks for power utilities to formulate grid planning and scheduling plans. Considering the temporal characteristics of the load data, in order to improve the prediction accuracy of power load, a short-term load forecasting model is established based on multi-branch residual gated convolution neural network (RGCNN). Firstly, the multi-branch residual gated convolution neural network is used to extract the weekly cycle, daily cycle and the nearest neighbor cycle of the historical load data. Secondly, the attention mechanism is used to distribute the weight reasonably to increase the nonlinear fitting ability of the model. Finally, the load forecasting result is output after normalized exponential function calculation. Experiments are carried out with the data of a power competition in 2016. Compared with the four typical forecasting models, the proposed model provides the prediction result with the MAPE evaluation indicator decreased by 0.02%-0.70%, which verifies the effectiveness of the proposed model in improving the forecasting accuracy.

Key words: short-term load forecasting, multi-branch neural network, RGCNN, attention mechanism, feature extraction

FAN Jiangchuan, YU Haozheng, LIU Huiting, YANG Lijun, AN Jiakun. Short-Term Load Forecasting Based on Multi-branch Residual Gated Convolution Neural Network[J]. Electric Power, 2022, 55(11): 155-162,174.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202112004

https://www.electricpower.com.cn/EN/Y2022/V55/I11/155

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