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.
Subject
Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction
Reference38 articles.
1. Uncertainty Forecasting in a Nutshell: Prediction Models Designed to Prevent Significant Errors;IEEE Power Energy Mag.,2017
2. A Study of Load Demand Forecasting Models in Electric Power System Operation and Planning;GMSARN Int. J.,2016
3. Performance analysis of short-term electricity demand with atmospheric variables;Energies,2018
4. Short-term electricity demand forecasting: Impact analysis of temperature for Thailand;Energies,2020
5. A note on the validity of cross-validation for evaluating autoregressive time series prediction;Comput. Stat. Data Anal.,2018
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