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Electricity Demand Modelling with Genetic Programming

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Progress in Artificial Intelligence (EPIA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9273))

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Abstract

Load forecasting is a critical task for all the operations of power systems. Especially during hot seasons, the influence of weather on energy demand may be strong, principally due to the use of air conditioning and refrigeration. This paper investigates the application of Genetic Programming on day-ahead load forecasting, comparing it with Neural Networks, Neural Networks Ensembles and Model Trees. All the experimentations have been performed on real data collected from the Italian electric grid during the summer period. Results show the suitability of Genetic Programming in providing good solutions to this problem. The advantage of using Genetic Programming, with respect to the other methods, is its ability to produce solutions that explain data in an intuitively meaningful way and that could be easily interpreted by a human being. This fact allows the practitioner to gain a better understanding of the problem under exam and to analyze the interactions between the features that characterize it.

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Author information

Authors and Affiliations

  1. NOVA IMS, Universidade Nova de Lisboa, 1070-312, Lisboa, Portugal

    Mauro Castelli & Leonardo Vanneschi

  2. Energy and Environment Modeling Technical Unit (UTMEA), ENEA, Casaccia Research Center, 00123, Roma, Italy

    Matteo De Felice

  3. Dipartimento di Informatica, Sistemistica E Comunicazione, Università Degli Studi di Milano Bicocca, 20126, Milano, Italy

    Luca Manzoni

Authors
  1. Mauro Castelli
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  2. Matteo De Felice
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  3. Luca Manzoni
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  4. Leonardo Vanneschi
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Corresponding author

Correspondence to Luca Manzoni .

Editor information

Editors and Affiliations

  1. ISEC - Coimbra Institute of Engineering, Polytechnic Institute of Coimbra, Coimbra, Portugal

    Francisco Pereira

  2. CIUSC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Penousal Machado

  3. CIUSC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Ernesto Costa

  4. CIUSC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Amílcar Cardoso

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© 2015 Springer International Publishing Switzerland

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Castelli, M., De Felice, M., Manzoni, L., Vanneschi, L. (2015). Electricity Demand Modelling with Genetic Programming. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 2015. Lecture Notes in Computer Science(), vol 9273. Springer, Cham. https://doi.org/10.1007/978-3-319-23485-4_22

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  • DOI: https://doi.org/10.1007/978-3-319-23485-4_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23484-7

  • Online ISBN: 978-3-319-23485-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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