Comparison of Conventional Techniques for House Electricity Consumption Forecasting

Centeno S.P.; Alatrista-Salas H. · International Journal of Advanced Computer Science and Applications (2025) · DOI: 10.14569/ijacsa.2025.01606108

Resumen

Electricity consumption monitoring is the automated process of recording, processing, and analyzing electricity usage in real time to make informed decisions. This research aims to implement an artificial intelligence- and deep learningbased methodology to forecast monthly electricity consumption in Tacna, Peru, and generate decision-making indicators. To this end, we used electricity consumption records from Electrosur S.A., the company responsible for electricity distribution and marketing in the departments of Tacna and Moquegua, from February 2015 to December 2022 (a total of 95 months). We compared three artificial intelligence models in this context: (i) eXtreme Gradient Boosting (XGBoost), (ii) Light Gradient Boosting (LGBM), and (iii) Prophet. While all models effectively forecasted electricity consumption, the Prophet model demonstrated superior performance, achieving a mean absolute percentage error (MAPE) of 0.7% compared to actual consumption values. Additionally, the study discusses the potential of recurrent neural networks to further enhance predictive accuracy. © (2025) (Science and Information Organization) All rights reserved.

Instituciones: Escuela de Posgrado Newman