Electricity Energy Demand Prediction Using Computational Intelligence Techniques

Abstract

Energy is an important pillar for the economic development of a country. The demand for electricity is something that continues to grow, one of the contributing factors is the emergence of various technological equipment and the consequent use by the population. There are several resources that can be exploited to generate electricity, with hydroelectric power stations being one of the most used resources. As electrical energy cannot be stored, there is a need to estimate its consumption, looking for a way to meet this energy demand. In this context, this study seeks to apply machine learning techniques, using the Grey Wolf Optimization (GWO) meta-heuristic to optimize regression models, to predict the demand for electricity in Brazil, and it aims to estimate how much energy should be produced. For the predictions, the period between the years 2017 to 2022 was used, totaling around 2,190 samples. The methodology involves pre-processing, crossvalidation, parameters optimization and regression. The results show that Random Forest performed well in the experiments carried out, presenting a coefficient of determination (R2) of 0.8751, Root Mean Squared Error (RMSE) of 0.0554 and Mean Absolute Error (MAE) of 0.0348 in the best model.

Keywords

• electric energy
• machine learning
• meta-heuristic
• gray wolf optimization