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Extraction of synoptic pressure patterns for long-term wind speed estimation in wind farms using evolutionary computing

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Listed:
  • Carro-Calvo, L.
  • Salcedo-Sanz, S.
  • Kirchner-Bossi, N.
  • Portilla-Figueras, A.
  • Prieto, L.
  • Garcia-Herrera, R.
  • Hernández-Martín, E.

Abstract

In this paper we present an evolutionary approach for the problem of discovering pressure patterns under a quality measure related to wind speed and direction. This clustering problem is specially interesting for companies involving in the management of wind farms, since it can be useful for analysis of results of the wind farm in a given period and also for long-term wind speed prediction. The proposed evolutionary algorithm is based on a specific encoding of the problem, which uses a dimensional reduction of the problem. With this special encoding, the required centroids are evolved together with some other parameters of the algorithm. We define a specific crossover operator and two different mutations in order to improve the evolutionary search of the proposed approach. In the experimental part of the paper, we test the performance of our approach in a real problem of pressure pattern extraction in the Iberian Peninsula, using a wind speed and direction series in a wind farm in the center of Spain. We compare the performance of the proposed evolutionary algorithm with that of an existing weather types (WT) purely meteorological approach, and we show that the proposed evolutionary approach is able to obtain better results than the WT approach.

Suggested Citation

  • Carro-Calvo, L. & Salcedo-Sanz, S. & Kirchner-Bossi, N. & Portilla-Figueras, A. & Prieto, L. & Garcia-Herrera, R. & Hernández-Martín, E., 2011. "Extraction of synoptic pressure patterns for long-term wind speed estimation in wind farms using evolutionary computing," Energy, Elsevier, vol. 36(3), pages 1571-1581.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:3:p:1571-1581
    DOI: 10.1016/j.energy.2011.01.001
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    1. Mellit, A. & Kalogirou, S.A. & Hontoria, L. & Shaari, S., 2009. "Artificial intelligence techniques for sizing photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(2), pages 406-419, February.
    2. Kusiak, Andrew & Zheng, Haiyang, 2010. "Optimization of wind turbine energy and power factor with an evolutionary computation algorithm," Energy, Elsevier, vol. 35(3), pages 1324-1332.
    3. Vaisakh, K. & Srinivas, L.R., 2010. "A genetic evolving ant direction DE for OPF with non-smooth cost functions and statistical analysis," Energy, Elsevier, vol. 35(8), pages 3155-3171.
    4. Li, Gong & Shi, Jing, 2010. "Application of Bayesian model averaging in modeling long-term wind speed distributions," Renewable Energy, Elsevier, vol. 35(6), pages 1192-1202.
    5. Mohandes, M.A. & Halawani, T.O. & Rehman, S. & Hussain, Ahmed A., 2004. "Support vector machines for wind speed prediction," Renewable Energy, Elsevier, vol. 29(6), pages 939-947.
    6. Toffolo, A. & Lazzaretto, A., 2002. "Evolutionary algorithms for multi-objective energetic and economic optimization in thermal system design," Energy, Elsevier, vol. 27(6), pages 549-567.
    7. Jursa, René & Rohrig, Kurt, 2008. "Short-term wind power forecasting using evolutionary algorithms for the automated specification of artificial intelligence models," International Journal of Forecasting, Elsevier, vol. 24(4), pages 694-709.
    8. Liao, Chiung-Chou, 2010. "Genetic k-means algorithm based RBF network for photovoltaic MPP prediction," Energy, Elsevier, vol. 35(2), pages 529-536.
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    Cited by:

    1. Wang, Jianzhou & Qin, Shanshan & Zhou, Qingping & Jiang, Haiyan, 2015. "Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China," Renewable Energy, Elsevier, vol. 76(C), pages 91-101.
    2. Sarrias-Mena, Raúl & Fernández-Ramírez, Luis M. & García-Vázquez, Carlos Andrés & Jurado, Francisco, 2014. "Fuzzy logic based power management strategy of a multi-MW doubly-fed induction generator wind turbine with battery and ultracapacitor," Energy, Elsevier, vol. 70(C), pages 561-576.
    3. Nicolas Kirchner-Bossi & Fernando Porté-Agel, 2018. "Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model," Energies, MDPI, vol. 11(12), pages 1-26, November.
    4. Wang, Jian-Zhou & Wang, Yun & Jiang, Ping, 2015. "The study and application of a novel hybrid forecasting model – A case study of wind speed forecasting in China," Applied Energy, Elsevier, vol. 143(C), pages 472-488.
    5. Kusiak, Andrew & Zhang, Zijun & Verma, Anoop, 2013. "Prediction, operations, and condition monitoring in wind energy," Energy, Elsevier, vol. 60(C), pages 1-12.
    6. Kirchner-Bossi, N. & Prieto, L. & García-Herrera, R. & Carro-Calvo, L. & Salcedo-Sanz, S., 2013. "Multi-decadal variability in a centennial reconstruction of daily wind," Applied Energy, Elsevier, vol. 105(C), pages 30-46.
    7. Wen, Songkang & Li, Yanting & Su, Yan, 2022. "A new hybrid model for power forecasting of a wind farm using spatial–temporal correlations," Renewable Energy, Elsevier, vol. 198(C), pages 155-168.

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