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Wind speed forecasting approach based on Singular Spectrum Analysis and Adaptive Neuro Fuzzy Inference System

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  • Moreno, Sinvaldo Rodrigues
  • dos Santos Coelho, Leandro

Abstract

As a promising renewable energy source, wind power has environmental benefits, as well as economic and social ones. Due these characteristics, wind farm has grown fast in the last five years, and in some countries, it has already surpassed conventional sources, such as hydro and coal plants. However, owing to the uncertainty of wind speed, it is essential to build an accurate forecasting model for large-scale wind power penetration. This study proposes a hybrid approach that combines the Singular Spectrum Analysis (SSA), which rarely presents application in literature on wind speed forecasting, and a Computing Natural paradigm called Adaptive Neuro Fuzzy Inference System (ANFIS). The SSA decomposes the original wind speed into various components, so these components are pre-processed regarding to the level of original wind series information remained. The main components selected to reconstruct the original series have in their structure the information about trend and harmonic components. The final remaining components grouped are labeled as noise. The ANFIS model uses these two information to construct the model applied to forecasting the next wind speed value. In this way, the hybrid model can learn the trend and the harmonic structure of the wind time series. Experimental results show that prediction errors are significantly reduced using the proposed technique to perform 10min one-step-ahead and k -step-ahead wind speed forecast.

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  • Moreno, Sinvaldo Rodrigues & dos Santos Coelho, Leandro, 2018. "Wind speed forecasting approach based on Singular Spectrum Analysis and Adaptive Neuro Fuzzy Inference System," Renewable Energy, Elsevier, vol. 126(C), pages 736-754.
    • Handle: RePEc:eee:renene:v:126:y:2018:i:c:p:736-754
    DOI: 10.1016/j.renene.2017.11.089
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    References listed on IDEAS

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    16. Shang, Zhihao & He, Zhaoshuang & Chen, Yao & Chen, Yanhua & Xu, MingLiang, 2022. "Short-term wind speed forecasting system based on multivariate time series and multi-objective optimization," Energy, Elsevier, vol. 238(PC).
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    18. Moreno, Sinvaldo Rodrigues & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2021. "Hybrid multi-stage decomposition with parametric model applied to wind speed forecasting in Brazilian Northeast," Renewable Energy, Elsevier, vol. 164(C), pages 1508-1526.
    19. Wei, Nan & Yin, Lihua & Li, Chao & Wang, Wei & Qiao, Weibiao & Li, Changjun & Zeng, Fanhua & Fu, Lingdi, 2022. "Short-term load forecasting using detrend singular spectrum fluctuation analysis," Energy, Elsevier, vol. 256(C).
    20. Zhengwei Huang & Jin Huang & Jintao Min, 2022. "SSA-LSTM: Short-Term Photovoltaic Power Prediction Based on Feature Matching," Energies, MDPI, vol. 15(20), pages 1-16, October.
    21. Zhou, Qingguo & Wang, Chen & Zhang, Gaofeng, 2019. "Hybrid forecasting system based on an optimal model selection strategy for different wind speed forecasting problems," Applied Energy, Elsevier, vol. 250(C), pages 1559-1580.
    22. Méndez-Gordillo, Alma Rosa & Campos-Amezcua, Rafael & Cadenas, Erasmo, 2022. "Wind speed forecasting using a hybrid model considering the turbulence of the airflow," Renewable Energy, Elsevier, vol. 196(C), pages 422-431.
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    24. da Silva, Ramon Gomes & Ribeiro, Matheus Henrique Dal Molin & Moreno, Sinvaldo Rodrigues & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2021. "A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting," Energy, Elsevier, vol. 216(C).

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