Robles, JoseSotelo-Valer, FreedyNahui-Ortiz, JohnnyLopez-Cordova, Jorge2021-08-172022-02-222021-08-172022-02-222021-07978-958-52071-8-92414-6390http://laccei.org/LACCEI2021-VirtualEdition/meta/FP219.htmlhttp://dx.doi.org/10.18687/LACCEI2021.1.1.219https://axces.info/handle/10.18687/20210101_219As 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 for energy demand forecast. Considering that data of energy demand per year is only available since 1980, resulting in a small dataset, Leave-One-Out Cross Validation method was used in order to measure the performance of the models with unseen data. After training all models, in terms of econometrics, models based on long term variables shows to be more robust than models with 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 data.EnglishLACCEI Licensehttps://laccei.org/blog/copyright-laccei-papers/Energy Demand ForecastMachine LearningXGBoostCross ValidationDecision TreesEconometric ModelsArtificial IntelligenceArticle