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Forecasting short-term electricity load using hybrid support vector regression with grey catastrophe and random forest modeling

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  • Fan, Guo-Feng
  • Yu, Meng
  • Dong, Song-Qiao
  • Yeh, Yi-Hsuan
  • Hong, Wei-Chiang

Abstract

This paper develops a novel short-term load forecasting model that hybridizes several machine learning methods, such as support vector regression (SVR), grey catastrophe (GC (1,1)), and random forest (RF) modeling. The modeling process is based on the minimization of both SVR and risk. GC is used to process and extract catastrophe points in the long term to reduce randomness. RF is used to optimize forecasting performance by exploiting its superior optimization capability. The proposed SVR-GC-RF model has higher forecasting accuracy (MAPE values are 6.35% and 6.21%, respectively) using electric loads from Australian-Energy-Market-Operator; it can provide analytical support to forecast electricity consumption accurately.

Suggested Citation

  • Fan, Guo-Feng & Yu, Meng & Dong, Song-Qiao & Yeh, Yi-Hsuan & Hong, Wei-Chiang, 2021. "Forecasting short-term electricity load using hybrid support vector regression with grey catastrophe and random forest modeling," Utilities Policy, Elsevier, vol. 73(C).
    • Handle: RePEc:eee:juipol:v:73:y:2021:i:c:s0957178721001284
    DOI: 10.1016/j.jup.2021.101294
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    References listed on IDEAS

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