Forecasting Short-Term Electricity Load Using Validated Ensemble Learning

Chatum Sankalpa, Somsak Kittipiyakul () and Seksan Laitrakun
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Chatum Sankalpa: Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12000, Thailand
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

Energies, 2022, vol. 15, issue 22, 1-30

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.

Keywords: short-term load forecasting; time series forecasting model validation; ensemble learning; accuracy improvement; Thailand EGAT dataset (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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