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Optimization of Feedforward Neural Networks Using an Improved Flower Pollination Algorithm for Short-Term Wind Speed Prediction

Author

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  • Yidi Ren

    (State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin 300131, China
    Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, School of Electrical Engineering, Hebei University of Technology, Tianjin 300131, China)

  • Hua Li

    (State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin 300131, China
    Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, School of Electrical Engineering, Hebei University of Technology, Tianjin 300131, China)

  • Hsiung-Cheng Lin

    (Department of Electronic Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan)

Abstract

It is well known that the inherent instability of wind speed may jeopardize the safety and operation of wind power generation, consequently affecting the power dispatch efficiency in power systems. Therefore, accurate short-term wind speed prediction can provide valuable information to solve the wind power grid connection problem. For this reason, the optimization of feedforward (FF) neural networks using an improved flower pollination algorithm is proposed. First of all, the empirical mode decomposition method is devoted to decompose the wind speed sequence into components of different frequencies for decreasing the volatility of the wind speed sequence. Secondly, a back propagation neural network is integrated with the improved flower pollination algorithm to predict the changing trend of each decomposed component. Finally, the predicted values of each component can get into an overlay combination process and achieve the purpose of accurate prediction of wind speed. Compared with major existing neural network models, the performance tests confirm that the average absolute error using the proposed algorithm can be reduced up to 3.67%.

Suggested Citation

  • Yidi Ren & Hua Li & Hsiung-Cheng Lin, 2019. "Optimization of Feedforward Neural Networks Using an Improved Flower Pollination Algorithm for Short-Term Wind Speed Prediction," Energies, MDPI, vol. 12(21), pages 1-17, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:21:p:4126-:d:281415
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    References listed on IDEAS

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    Cited by:

    1. Evangelos Spiliotis & Fotios Petropoulos & Konstantinos Nikolopoulos, 2020. "The Impact of Imperfect Weather Forecasts on Wind Power Forecasting Performance: Evidence from Two Wind Farms in Greece," Energies, MDPI, vol. 13(8), pages 1-18, April.
    2. Bartłomiej Mroczek & Paweł Pijarski, 2022. "Machine Learning in Operating of Low Voltage Future Grid," Energies, MDPI, vol. 15(15), pages 1-30, July.

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