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Using Deep Learning to Predict Complex Systems: A Case Study in Wind Farm Generation

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  • J. M. Torres
  • R. M. Aguilar

Abstract

Making every component of an electrical system work in unison is being made more challenging by the increasing number of renewable energies used, the electrical output of which is difficult to determine beforehand. In Spain, the daily electricity market opens with a 12-hour lead time, where the supply and demand expected for the following 24 hours are presented. When estimating the generation, energy sources like nuclear are highly stable, while peaking power plants can be run as necessary. Renewable energies, however, which should eventually replace peakers insofar as possible, are reliant on meteorological conditions. In this paper we propose using different deep-learning techniques and architectures to solve the problem of predicting wind generation in order to participate in the daily market, by making predictions 12 and 36 hours in advance. We develop and compare various estimators based on feedforward, convolutional, and recurrent neural networks. These estimators were trained and validated with data from a wind farm located on the island of Tenerife. We show that the best candidates for each type are more precise than the reference estimator and the polynomial regression currently used at the wind farm. We also conduct a sensitivity analysis to determine which estimator type is most robust to perturbations. An analysis of our findings shows that the most accurate and robust estimators are those based on feedforward neural networks with a SELU activation function and convolutional neural networks.

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  • J. M. Torres & R. M. Aguilar, 2018. "Using Deep Learning to Predict Complex Systems: A Case Study in Wind Farm Generation," Complexity, Hindawi, vol. 2018, pages 1-10, April.
  • Handle: RePEc:hin:complx:9327536
    DOI: 10.1155/2018/9327536
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
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    1. Kisvari, Adam & Lin, Zi & Liu, Xiaolei, 2021. "Wind power forecasting – A data-driven method along with gated recurrent neural network," Renewable Energy, Elsevier, vol. 163(C), pages 1895-1909.
    2. Csaba Sidor & Branislav Kršák & Ľubomír Štrba & Michal Cehlár & Samer Khouri & Michal Stričík & Jaroslav Dugas & Ján Gajdoš & Barbora Bolechová, 2019. "Can Location-Based Social Media and Online Reservation Services Tell More about Local Accommodation Industries than Open Governmental Data?," Sustainability, MDPI, vol. 11(21), pages 1-21, October.
    3. David Flores-Ruiz & Adolfo Elizondo-Salto & María de la O. Barroso-González, 2021. "Using Social Media in Tourist Sentiment Analysis: A Case Study of Andalusia during the Covid-19 Pandemic," Sustainability, MDPI, vol. 13(7), pages 1-19, March.
    4. Sergio Velázquez Medina & José A. Carta & Ulises Portero Ajenjo, 2019. "Performance Sensitivity of a Wind Farm Power Curve Model to Different Signals of the Input Layer of ANNs: Case Studies in the Canary Islands," Complexity, Hindawi, vol. 2019, pages 1-11, March.

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