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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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    9. 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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    14. 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.
    15. Zhang, Shuai & Chen, Yong & Xiao, Jiuhong & Zhang, Wenyu & Feng, Ruijun, 2021. "Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism," Renewable Energy, Elsevier, vol. 174(C), pages 688-704.
    16. Yang, Qiuling & Deng, Changhong & Chang, Xiqiang, 2022. "Ultra-short-term / short-term wind speed prediction based on improved singular spectrum analysis," Renewable Energy, Elsevier, vol. 184(C), pages 36-44.
    17. Majidi Nezhad, M. & Heydari, A. & Pirshayan, E. & Groppi, D. & Astiaso Garcia, D., 2021. "A novel forecasting model for wind speed assessment using sentinel family satellites images and machine learning method," Renewable Energy, Elsevier, vol. 179(C), pages 2198-2211.
    18. Aasim, & Singh, S.N. & Mohapatra, Abheejeet, 2019. "Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting," Renewable Energy, Elsevier, vol. 136(C), pages 758-768.
    19. Majidi Nezhad, M. & Heydari, A. & Groppi, D. & Cumo, F. & Astiaso Garcia, D., 2020. "Wind source potential assessment using Sentinel 1 satellite and a new forecasting model based on machine learning: A case study Sardinia islands," Renewable Energy, Elsevier, vol. 155(C), pages 212-224.
    20. Mohammadzadeh Bina, Saeid & Jalilinasrabady, Saeid & Fujii, Hikari & Farabi-Asl, Hadi, 2018. "A comprehensive approach for wind power plant potential assessment, application to northwestern Iran," Energy, Elsevier, vol. 164(C), pages 344-358.
    21. Akylas Stratigakos & Athanasios Bachoumis & Vasiliki Vita & Elias Zafiropoulos, 2021. "Short-Term Net Load Forecasting with Singular Spectrum Analysis and LSTM Neural Networks," Energies, MDPI, vol. 14(14), pages 1-13, July.
    22. Li, Yanfei & Shi, Huipeng & Han, Fengze & Duan, Zhu & Liu, Hui, 2019. "Smart wind speed forecasting approach using various boosting algorithms, big multi-step forecasting strategy," Renewable Energy, Elsevier, vol. 135(C), pages 540-553.
    23. Wang, Yun & Xu, Houhua & Song, Mengmeng & Zhang, Fan & Li, Yifen & Zhou, Shengchao & Zhang, Lingjun, 2023. "A convolutional Transformer-based truncated Gaussian density network with data denoising for wind speed forecasting," Applied Energy, Elsevier, vol. 333(C).
    24. 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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