  Citation: Sankalpa, C.; Kittipiyakul, S.; Laitrakun, S. Forecasting Short-Term Electricity Load Using Validated Ensemble Learning. Energies 2022, 15, 8567. https:// doi.org/10.3390/en15228567 Academic Editors: Antonio Gabaldón, María Carmen Ruiz-Abellón and Luis Alfredo Fernández-Jiménez Received: 6 October 2022 Accepted: 9 November 2022 Published: 16 November 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). energies Article Forecasting Short-Term Electricity Load Using Validated Ensemble Learning Chatum Sankalpa 1,2 , Somsak Kittipiyakul 1, * and Seksan Laitrakun 1 1 Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12000, Thailand 2 Department of Electrical and Information Engineering, Faculty of Engineering, University of Ruhuna, Galle 80000, Sri Lanka * Correspondence: somsak@siit.tu.ac.th 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 1. Introduction The increased energy demand due to the rise in the population over the past few decades has been an eye-opener for the efficient use of energy all over the world. One of the critical objectives of energy forecasting is to allocate a sufficient and efficient energy supply to cater for the future demand. Many countries seek alternative energy resources to balance supply and demand [1]. Therefore, forecasting the required demand at least for a 1 day ahead has become a popular theme among energy providers to maintain that equilibrium. Forecasting is divided into three subsections according to the prediction horizon: short-term load forecasting (STLF), medium-term load forecasting (MTLF), and long-term load forecasting (LTLF) [2]. Each plays a different role in the power system, benefiting supply and demand-side management. Accurate forecasting of a country’s short-term electric energy demand is the key to making day-to-day decisions on hourly/day-ahead demand compared to the medium-term and long-term forecasts. Forecasting the electric energy demand is carried out by developing models using historical data, and many factors, such as climate conditions, calendar parameters, and some seasonal features [3]. Since Thailand is a tropical country, its electric demand is heavily influenced by climate and weather conditions, along with many public, religious, and long holidays. The limited research on Thai data found in the literature, which will be further elaborated in Section 2, suggests that the accuracy of short-term forecasting can be further improved with a better selection of features and using appropriate models. Machine learning (ML) plays a leading role when predicting electricity demand com- pared to classical methods, such as statistical time series analysis and smoothing techniques. Energies 2022, 15, 8567. https://doi.org/10.3390/en15228567 https://www.mdpi.com/journal/energies