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

Citations

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Cited by:

  1. Wang, Cong & Zhang, Hongli & Ma, Ping, 2020. "Wind power forecasting based on singular spectrum analysis and a new hybrid Laguerre neural network," Applied Energy, Elsevier, vol. 259(C).
  2. Khodayar, Mahdi & Saffari, Mohsen & Williams, Michael & Jalali, Seyed Mohammad Jafar, 2022. "Interval deep learning architecture with rough pattern recognition and fuzzy inference for short-term wind speed forecasting," Energy, Elsevier, vol. 254(PB).
  3. 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.
  4. Lasemi, Mohammad Ali & Arabkoohsar, Ahmad, 2020. "Optimal operating strategy of high-temperature heat and power storage system coupled with a wind farm in energy market," Energy, Elsevier, vol. 210(C).
  5. Wu, Binrong & Wang, Lin & Zeng, Yu-Rong, 2022. "Interpretable wind speed prediction with multivariate time series and temporal fusion transformers," Energy, Elsevier, vol. 252(C).
  6. 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.
  7. Ullah, Sajid & Chen, Xi & Han, Han & Wu, Junhao & Dong, Jinghan & Liu, Ruiqing & Ding, Weijie & Liu, Min & Li, Qingli & Qi, Honggang & Huang, Yonggui & Yu, Philip Lh, 2025. "A novel hybrid ensemble approach for wind speed forecasting with dual-stage decomposition strategy using optimized GRU and transformer models," Energy, Elsevier, vol. 329(C).
  8. 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.
  9. 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).
  10. 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.
  11. 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.
  12. 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.
  13. 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).
  14. Yuan-Kang Wu & Cheng-Liang Huang & Quoc-Thang Phan & Yuan-Yao Li, 2022. "Completed Review of Various Solar Power Forecasting Techniques Considering Different Viewpoints," Energies, MDPI, vol. 15(9), pages 1-22, May.
  15. Laeeq Aslam & Runmin Zou & Ebrahim Shahzad Awan & Sayyed Shahid Hussain & Kashish Ara Shakil & Mudasir Ahmad Wani & Muhammad Asim, 2025. "Hardware-Centric Exploration of the Discrete Design Space in Transformer–LSTM Models for Wind Speed Prediction on Memory-Constrained Devices," Energies, MDPI, vol. 18(9), pages 1-21, April.
  16. 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.
  17. Emeksiz, Cem & Tan, Mustafa, 2022. "Wind speed estimation using novelty hybrid adaptive estimation model based on decomposition and deep learning methods (ICEEMDAN-CNN)," Energy, Elsevier, vol. 249(C).
  18. 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.
  19. 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.
  20. 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.
  21. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
  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).
  25. Nathan Oaks Farrar & Mohd Hasan Ali & Dipankar Dasgupta, 2023. "Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review," Energies, MDPI, vol. 16(3), pages 1-25, February.
  26. 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.
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