Forecasting of Energy Consumption in the Philippines Using Machine Learning Algorithms

Abstract

In the Philippines, usage of energy has been steadily increasing over the years, however, the advent of the COVID-19 pandemic brought about unforeseen changes to these parameters. By using machine learning algorithms, energy predictions can be more properly assessed, and corresponding measures can be put into place. The Monthly and Quarterly Market Assessment Report of Wholesale Electricity Spot Market (WESM), governed by Philippines Electricity Market Corporation (PEMC)’s data was analyzed using four machine learning algorithms, namely, Random Forest, XGBoost, Linear Regression, and Support Vector Regression (SVR) to determine the best algorithm in predicting energy consumption within the pre-pandemic and pandemic periods. It was found that the Pre-pandemic (Period 1) data was most accurately predicted by the XGBoost model, having a Root Mean Square Error (RMSE) of 366.691 and Mean Percentage Error (MAPE) of 0.044, while the Pandemic (Period 2) data was most accurately predicted by the Random Forest Model from its RMSE of 687.665 and MAPE of 0.061. While the poorest performing model for both these periods was the SVR, getting an RMSE of 431.366 and 982.202 to the respective periods. The results show how developing tree-based predictive models, XGBoost and Random Forest Models, are significant in forecasting energy consumption in the Philippines, and is therefore also beneficial for future studies that aim to engage in crafting energy conservation and efficiency policies for economic growth.