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A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting

Author

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  • Yuyang Gao

    (School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China)

  • Chao Qu

    (School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China)

  • Kequan Zhang

    (Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China)

Abstract

With increasing importance being attached to big data mining, analysis, and forecasting in the field of wind energy, how to select an optimization model to improve the forecasting accuracy of the wind speed time series is not only an extremely challenging problem, but also a problem of concern for economic forecasting. The artificial intelligence model is widely used in forecasting and data processing, but the individual back-propagation artificial neural network cannot always satisfy the time series forecasting needs. Thus, a hybrid forecasting approach has been proposed in this study, which consists of data preprocessing, parameter optimization and a neural network for advancing the accuracy of short-term wind speed forecasting. According to the case study, in which the data are collected from Peng Lai, a city located in China, the simulation results indicate that the hybrid forecasting method yields better predictions compared to the individual BP, which indicates that the hybrid method exhibits stronger forecasting ability.

Suggested Citation

  • Yuyang Gao & Chao Qu & Kequan Zhang, 2016. "A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 9(10), pages 1-28, September.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:10:p:757-:d:78657
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    References listed on IDEAS

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    Cited by:

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    6. Francisco Martínez-Álvarez & Alicia Troncoso & José C. Riquelme, 2017. "Recent Advances in Energy Time Series Forecasting," Energies, MDPI, vol. 10(6), pages 1-3, June.
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