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

Autor: Bingjie Jin, Guihua Zeng, Zhilin Lu, Hongqiao Peng, Shuxin Luo, Xinhe Yang, Haojun Zhu, Mingbo Liu
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Energies, Vol 15, Iss 20, p 7584 (2022)
Druh dokumentu: article
ISSN: 1996-1073
DOI: 10.3390/en15207584
Popis: 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.
Databáze: Directory of Open Access Journals
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