Abstract
The world population has been rising on a large scale, and this directly reflects on the electricity consumption. In this scenario, techniques for accurately forecasting energy consumption are particularly important, since these data can be applied in decision-making and good planning aimed at providing constant and reliable energy. Forecasting energy consumption with the most accurate value possible is not a trivial task and depends on some factors. One of the most recent works dealing with the subject at the Brazil presented a fuzzy logic-based prediction model using consumption, Gross Domestic Product index (GDP), and population and obtained good results. This work aims to evaluate, through an experimental study, the performance of classical regression techniques—linear regression, multilayer perceptron, and support vector regression—in energy consumption forecast in the Brazilian scenario. Also, we verified whether the inclusion of additional socioeconomic data could contribute to obtaining a more efficient model. When compared to the results available in the literature, our approach demonstrated superior performance in some situations.
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Vasconcelos, L., Viterbo, J., Coelho, I.M., Meirelles da Silva, J.M. (2019). An Experimental Study of Regression Techniques for the Residential Energy Consumption Forecast in the Brazilian Scenario. In: Nazário Coelho, V., Machado Coelho, I., A.Oliveira, T., Ochi, L.S. (eds) Smart and Digital Cities. Urban Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-12255-3_8
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