Long-Term Energy Demand Forecasting in Thailand with Ensemble Prediction Model

Abstract

This research has proposed to utilize the combination of Machine Learning models (ML models) to optimally forecast the energy demand in Thailand. The various ML models are explored in which the individual and the combination of ML models are each optimized and evaluated for their best achievable performances. Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) are utilized to compare models' performances. A total of 4 ML models are executed, which include Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF) Ensemble and proposed Vote Ensemble models. The results show that, by means of ensemble or model combination, the Vote Ensemble model could perform well with the lowest RMSE for training and testing of 613.63 and 666.52 and the lowest MAPE of 3.59% accordingly while also using less execution time of 3 minutes and 56 seconds.