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Short-Term and Medium-Term Electricity Sales Forecasting Method Based on Deep Spatio-Temporal Residual Network

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Listed:
  • Min Cao

    (State Grid Shaanxi Electric Power Company Limited, Xi’an 710048, China)

  • Jinfeng Wang

    (State Grid Shaanxi Electric Power Company Limited Research Institute, Xi’an 710065, China)

  • Xiaochen Sun

    (State Grid Shaanxi Electric Power Company Limited Research Institute, Xi’an 710065, China)

  • Zhengmou Ren

    (State Grid Shaanxi Electric Power Company Limited Research Institute, Xi’an 710065, China)

  • Haokai Chai

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Jie Yan

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (NCEPU), School of Renewable Energy, North China Electric Power University, Beijing 102206, China)

  • Ning Li

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

Abstract

The forecasting of electricity sales is directly related to the power generation planning of power enterprises and the progress of the generation tasks. Aiming at the problem that traditional forecasting methods cannot properly deal with the actual data offset caused by external factors, such as the weather, season, and spatial attributes, this paper proposes a method of electricity sales forecasting based on a deep spatio-temporal residual network (ST-ResNet). The method not only relies on the temporal correlation of electricity sales data but also introduces the influence of external factors and spatial correlation, which greatly enhances the fitting degree of each parameter of the model. Moreover, the residual module and the convolution module are fused to effectively reduce the damage of the deep convolutional process to the training effectiveness. Finally, the three comparison experiments of the ultra-short term, short term and medium term show that the MAPE forecasted by the ST-ResNet model is at least 2.69% lower than that of the RNN and other classical Deep Learning models, that its RMSE is at least 36.2% lower, and that its MAD is at least 34.2% lower, which is more obvious than the traditional methods. The effectiveness and feasibility of the ST-ResNet model in the short-term forecasting of electricity sales are verified.

Suggested Citation

  • Min Cao & Jinfeng Wang & Xiaochen Sun & Zhengmou Ren & Haokai Chai & Jie Yan & Ning Li, 2022. "Short-Term and Medium-Term Electricity Sales Forecasting Method Based on Deep Spatio-Temporal Residual Network," Energies, MDPI, vol. 15(23), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:8844-:d:981796
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    References listed on IDEAS

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    1. Yuhong Xie & Yuzuru Ueda & Masakazu Sugiyama, 2021. "A Two-Stage Short-Term Load Forecasting Method Using Long Short-Term Memory and Multilayer Perceptron," Energies, MDPI, vol. 14(18), pages 1-17, September.
    2. Zhang, Yu & Li, Yanting & Zhang, Guangyao, 2020. "Short-term wind power forecasting approach based on Seq2Seq model using NWP data," Energy, Elsevier, vol. 213(C).
    3. Fotios Petropoulos & Evangelos Spiliotis, 2021. "The Wisdom of the Data: Getting the Most Out of Univariate Time Series Forecasting," Forecasting, MDPI, vol. 3(3), pages 1-20, June.
    4. Jiang, Weiheng & Wu, Xiaogang & Gong, Yi & Yu, Wanxin & Zhong, Xinhui, 2020. "Holt–Winters smoothing enhanced by fruit fly optimization algorithm to forecast monthly electricity consumption," Energy, Elsevier, vol. 193(C).
    5. Yuehjen E. Shao & Yi-Shan Tsai, 2018. "Electricity Sales Forecasting Using Hybrid Autoregressive Integrated Moving Average and Soft Computing Approaches in the Absence of Explanatory Variables," Energies, MDPI, vol. 11(7), pages 1-22, July.
    6. Shahid, Farah & Zameer, Aneela & Muneeb, Muhammad, 2021. "A novel genetic LSTM model for wind power forecast," Energy, Elsevier, vol. 223(C).
    Full references (including those not matched with items on IDEAS)

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