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Power-Load Forecasting Model Based on Informer and Its Application

Author

Listed:
  • Hongbin Xu

    (School of Information Engineering, Nanchang University, Nanchang 330031, China)

  • Qiang Peng

    (School of Information Engineering, Nanchang University, Nanchang 330031, China)

  • Yuhao Wang

    (School of Information Engineering, Nanchang University, Nanchang 330031, China
    Shangrao Normal University, Shangrao 334001, China)

  • Zengwen Zhan

    (State Grid Nanchang Power Supply Company, Nanchang 330031, China)

Abstract

Worldwide, the demand for power load forecasting is increasing. A multi-step power-load forecasting model is established based on Informer, which takes the historical load data as the input to realize the prediction of the power load in the future. The constructed model abandons the common recurrent neural network to deal with time-series problems, and uses the seq2seq structure with sparse self-attention mechanism as the main body, supplemented by specific input and output modules to deal with the long-range relationship in the time series, and makes effective use of the parallel advantages of the self-attention mechanism, so as to improve the prediction accuracy and prediction efficiency. The model is trained, verified and tested by using the power-load dataset of the Taoyuan substation in Nanchang. Compared with RNN, LSTM and LSTM with the attention mechanism and other common models based on a cyclic neural network, the results show that the prediction accuracy and efficiency of the Informer-based power-load forecasting model in 1440 time steps have certain advantages over cyclic neural network models.

Suggested Citation

  • Hongbin Xu & Qiang Peng & Yuhao Wang & Zengwen Zhan, 2023. "Power-Load Forecasting Model Based on Informer and Its Application," Energies, MDPI, vol. 16(7), pages 1-14, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:7:p:3086-:d:1109926
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    References listed on IDEAS

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    1. Che, JinXing & Wang, JianZhou, 2014. "Short-term load forecasting using a kernel-based support vector regression combination model," Applied Energy, Elsevier, vol. 132(C), pages 602-609.
    2. Lindberg, K.B. & Seljom, P. & Madsen, H. & Fischer, D. & Korpås, M., 2019. "Long-term electricity load forecasting: Current and future trends," Utilities Policy, Elsevier, vol. 58(C), pages 102-119.
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    Cited by:

    1. Zijing Dong & Boyi Fan & Fan Li & Xuezhi Xu & Hong Sun & Weiwei Cao, 2023. "TCN-Informer-Based Flight Trajectory Prediction for Aircraft in the Approach Phase," Sustainability, MDPI, vol. 15(23), pages 1-20, November.
    2. Xinjian Xiang & Tianshun Yuan & Guangke Cao & Yongping Zheng, 2024. "Short-Term Electric Load Forecasting Based on Signal Decomposition and Improved TCN Algorithm," Energies, MDPI, vol. 17(8), pages 1-21, April.

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