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Forecasting Short-Term Electricity Load Using Validated Ensemble Learning

Author

Listed:
  • Chatum Sankalpa

    (Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12000, Thailand
    Department of Electrical and Information Engineering, Faculty of Engineering, University of Ruhuna, Galle 80000, Sri Lanka)

  • Somsak Kittipiyakul

    (Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12000, Thailand)

  • Seksan Laitrakun

    (Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12000, Thailand)

Abstract

As short-term load forecasting is essential for the day-to-day operation planning of power systems, we built an ensemble learning model to perform such forecasting for Thai data. The proposed model uses voting regression (VR), producing forecasts with weighted averages of forecasts from five individual models: three parametric multiple linear regressors and two non-parametric machine-learning models. The regressors are linear regression models with gradient-descent (LR), ordinary least-squares (OLS) estimators, and generalized least-squares auto-regression (GLSAR) models. In contrast, the machine-learning models are decision trees (DT) and random forests (RF). To select the best model variables and hyper-parameters, we used cross-validation (CV) performance instead of the test data performance, which yielded overly good test performance. We compared various validation schemes and found that the Blocked-CV scheme gives the validation error closest to the test error. Using Blocked-CV, the test results show that the VR model outperforms all its individual predictors.

Suggested Citation

  • Chatum Sankalpa & Somsak Kittipiyakul & Seksan Laitrakun, 2022. "Forecasting Short-Term Electricity Load Using Validated Ensemble Learning," Energies, MDPI, vol. 15(22), pages 1-30, November.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8567-:d:974451
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    References listed on IDEAS

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