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A novel short-term electrical load forecasting framework with intelligent feature engineering

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  • Yu, Binbin
  • Li, Jianjing
  • Liu, Che
  • Sun, Bo

Abstract

The emergence of many new types of electrical loads, such as security systems and charging piles, has resulted in a diversified trend of building load-end components. This trend is coupled with chaotic influencing factors with time-scale differences such as multiple periods and special events, thereby causing difficulties in extracting multiple time-scale features (including regular electricity behavior features in a long term and random electricity behavior features in an ultrashort term) and reducing the quality of short-term forecasting. To this end, a two-level intelligent feature engineering (IFE) and serial multi-timescale forecasting framework is designed in this work. First, the first-level IFE is proposed to deal with weather and calendar effects and to extract regular electricity behavior features in a long term, thus helping the 24-h ahead forecasting model to obtain a stable forecast value of the load trend. Second, based on the day-ahead trend component forecasting (DATCF) value obtained in the previous step, the second-level IFE is proposed to extract the random electricity behavior features in an ultrashort term. The aim is to improve the forecasting capability of the 15-min ahead forecasting model for load fluctuation detail. This work eliminates redundant and irrelevant features through a two-level IFE and balances the stability for load trend forecasting and the accuracy for load fluctuation detail forecasting through a serial multi-timescale forecasting framework. Finally, the simulation results indicate that the root mean square error (RMSE) of the proposed method is less than that of the control group by 8.98–31.56 kW, and the RMSE of the proposed method decreased by 1.27–66.13 kW during peak hours with strong volatility.

Suggested Citation

  • Yu, Binbin & Li, Jianjing & Liu, Che & Sun, Bo, 2022. "A novel short-term electrical load forecasting framework with intelligent feature engineering," Applied Energy, Elsevier, vol. 327(C).
    • Handle: RePEc:eee:appene:v:327:y:2022:i:c:s0306261922013460
    DOI: 10.1016/j.apenergy.2022.120089
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    Cited by:

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    3. Jiang, Zongxi & Zhang, Luliang & Ji, Tianyao, 2023. "NSDAR: A neural network-based model for similar day screening and electric load forecasting," Applied Energy, Elsevier, vol. 349(C).

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