Long and Short Term Energy Demand Forecasting using XGBoost Models

Robles J.; Sotelo-Valer F.; Nahui-Ortiz J.; Lopez-Cordova J. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2021) · DOI: 10.18687/laccei2021.1.1.219

Resumen

As part of the technical studies in energy demand required by regulatory entities in Peru, this paper proposes the use of XGBoost Linear and Decision Trees models based on econometric long and short term variables to forecast the energy demand. Considering that data of energy demand per year is only available since 1980, which means a small dataset, Leave-One-Out Cross-Validation method was used in order to measure the performance of the models with unseen data. After the training stage, in terms of econometric variables, models based on long term variables show to be more robust than models with the short term ones. In addition, Decision Trees shows a better performance than Linear Models with a noticeable difference in the coefficient of determination for both training and test datasets.

Instituciones: Universidad Ricardo Palma; Universidad Nacional de Ingeniería