Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load

Bingjie Jin, Guihua Zeng, Zhilin Lu, Hongqiao Peng, Shuxin Luo, Xinhe Yang, Haojun Zhu and Mingbo Liu
Additional contact information
Bingjie Jin: Power Grid Planning Center of Guangdong Power Grid Company, Guangzhou 510080, China
Guihua Zeng: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
Zhilin Lu: Energy Development Research Institute, China Southern Power Grid, Guangzhou 510663, China
Hongqiao Peng: Power Grid Planning Center of Guangdong Power Grid Company, Guangzhou 510080, China
Shuxin Luo: Power Grid Planning Center of Guangdong Power Grid Company, Guangzhou 510080, China
Xinhe Yang: Energy Development Research Institute, China Southern Power Grid, Guangzhou 510663, China
Haojun Zhu: Energy Development Research Institute, China Southern Power Grid, Guangzhou 510663, China
Mingbo Liu: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China

Energies, 2022, vol. 15, issue 20, 1-20

Abstract: Accurate medium- and long-term electricity peak load forecasting is critical for power system operation, planning, and electricity trading. However, peak load forecasting is challenging because of the complex and nonlinear relationship between peak load and related factors. Here, we propose a hybrid LSTM–BPNN-to-BPNN model combining a long short-term memory network (LSTM) and back propagation neural network (BPNN) to separately extract the features of the historical data and future information. Their outputs are then concatenated to a vector and inputted into the next BPNN model to obtain the final prediction. We further analyze the peak load characteristics for reducing prediction error. To overcome the problem of insufficient annual data for training the model, all the input variables distributed over various time scales are converted into a monthly time scale. The proposed model is then trained to predict the monthly peak load after one year and the maximum value of the monthly peak load is selected as the predicted annual peak load. The comparison results indicate that the proposed method achieves a predictive accuracy superior to that of benchmark models based on a real-world dataset.

Keywords: medium- and long-term peak load forecasting; multi-source information; long short-term memory (LSTM); back propagation neural network (BPNN) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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