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An Experimental Study of Regression Techniques for the Residential Energy Consumption Forecast in the Brazilian Scenario

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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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Author information

Authors and Affiliations

  1. Instituto de Computação, Universidade Federal Fluminense, Niterói, RJ, Brazil

    Leonardo Vasconcelos & José Viterbo

  2. Instituto de Matemática e Estatística, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, RJ, Brazil

    Igor Machado Coelho

  3. Escola de Engenharia, Universidade Federal Fluminense, Niterói, RJ, Brazil

    João Marcos Meirelles da Silva

Authors
  1. Leonardo Vasconcelos
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  2. José Viterbo
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  3. Igor Machado Coelho
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  4. João Marcos Meirelles da Silva
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Corresponding author

Correspondence to Leonardo Vasconcelos .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, Universidade Federal Fluminense, Niterói, Rio de Janeiro, Brazil

    Vitor Nazário Coelho

  2. Instituto de Matemática e Estatística, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil

    Igor Machado Coelho

  3. Department of Engineering and Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain

    Thays A.Oliveira

  4. Institute of Computer Science, Universidade Federal Fluminense, Niterói, Rio de Janeiro, Brazil

    Luiz Satoru Ochi

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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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  • DOI: https://doi.org/10.1007/978-3-030-12255-3_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12254-6

  • Online ISBN: 978-3-030-12255-3

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