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Comparing different solutions for forecasting the energy production of a wind farm

  • S.I. : Advances in Bio-Inspired Intelligent Systems
  • Published:
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Abstract

The production of different renewable and non-renewable energies sources can be coordinated efficiently to avoid costly overproduction. For that, it is important to develop models for accurate energy production forecasting. The energy production of wind farms is extremely dependent on the meteorological conditions. In this paper, computational intelligence techniques were used to predict the production of energy in a wind farm. This study is held on publicly accessible climacteric and energy data for a wind farm in Galicia, Spain, with 24 turbines of 9 different models. Data preprocessing was performed in order to delete outliers caused by the maintenance and technical problems. Models of the following types were developed: artificial neural networks, support vector machines and adaptive neuro-fuzzy inference system models. Furthermore, the persistence method was used as a time series forecast baseline model. Overall, the developed computational intelligence models perform better than the baseline model, being adaptive neuro-fuzzy inference system the model with the best results: a ~ 5% performance improvement over the baseline model.

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Acknowledgements

Agradecimentos à ARDITI—Agência Regional para o Desenvolvimento da Investigação Tecnologia e Inovação através do apoio concedido no âmbito do Projeto M1420—09-5369-FSE-000001—Bolsa de Doutoramento. Acknowledgments to the Portuguese Foundation for Science and Technology for their support through Projeto Estratégico LA 9—UID/EEA/50009/2013. The work reported in this article was supported by national funds through Fundação para a Ciência e Tecnologia (FCT) with reference UID/CEC/50021/2013.

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Authors and Affiliations

  1. M-ITI, Madeira Interactive Technologies Institute, Funchal, Portugal

    Darío Baptista & Fernando Morgado-Dias

  2. INESC-ID, Instituto Superior Técnico, Lisbon, Portugal

    Darío Baptista & João Paulo Carvalho

  3. UMa, Universidade da Madeira, Funchal, Portugal

    Fernando Morgado-Dias

Authors
  1. Darío Baptista
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  2. João Paulo Carvalho
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  3. Fernando Morgado-Dias
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Correspondence to Darío Baptista.

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The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

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Baptista, D., Carvalho, J.P. & Morgado-Dias, F. Comparing different solutions for forecasting the energy production of a wind farm. Neural Comput & Applic 32, 15825–15833 (2020). https://doi.org/10.1007/s00521-018-3628-5

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  • DOI: https://doi.org/10.1007/s00521-018-3628-5

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