Abstract
Today, dependence on technology is increasing and, as a result, energy consumption has to keep up with this growth. To meet this demand, renewable energies are increasingly being used to produce more energy in a sustainable way, which has led to an increase in the load on the distribution network. Thus, with the exponential growth in dependence on renewable generation technologies, it is becoming increasingly common for studies to be carried out into consumption patterns in order to try to understand the needs of the population and thus make more rational and efficient use of energy. The aim of this article is to study, understand and explain the workings of some of the best forecasting methods available today for energy consumption patterns identification. Artificial neural networks, support vector machines, principal component analysis and even hierarchical clustering are some of the methods analyzed. © 2025 Elsevier B.V., All rights reserved.