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  journal={Journal of Cleaner Production},
  volume={296},
  pages={126564},
  year={2021},
  publisher={Elsevier}
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@article{wu2021ultra,
  title={Ultra-short-term multi-step wind power forecasting based on CNN-LSTM},
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  year={2021},
  publisher={Wiley Online Library}
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@article{neshat2022quaternion,
  title={Quaternion convolutional long short-term memory neural model with an adaptive decomposition method for wind speed forecasting: North aegean islands case studies},
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  journal={Energy Conversion and Management},
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  pages={115590},
  year={2022},
  publisher={Elsevier}
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@article{nguyen2022forecasting,
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  journal={Journal of Applied Science and Engineering},
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  number={8},
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  year={2022},
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  pages={14997--15010},
  year={2022},
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@article{aljanad2021neural,
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  journal={Energies},
  volume={14},
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  year={2021},
doi={https://doi.org/10.3390/en14041213},
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  year={2022},
doi={https://doi.org/10.13052/spee1048-5236.4132}
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doi={https://doi.org/10.1016/j.egyr.2022.10.402},
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@article{hossain2021very,
  title={Very short-term forecasting of wind power generation using hybrid deep learning model},
  author={Hossain, Md Alamgir and Chakrabortty, Ripon K and Elsawah, Sondoss and Ryan, Michael J},
  journal={Journal of Cleaner Production},
  volume={296},
  pages={126564},
  year={2021},
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}
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  title={BiLSTM network-based approach for solar irradiance forecasting in continental climate zones},
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  journal={Energies},
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  year={2022},
doi={https://doi.org/10.3390/en15062226},
  publisher={MDPI}
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  title={Short-term wind speed forecasting over complex terrain using linear regression models and multivariable LSTM and NARX networks in the Andes Mountains, Ecuador},
  author={L{\'o}pez, Germ{\'a}nico and Arboleya, Pablo},
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}
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  title={Renewable-integrated flexible carbon capture: a synergistic path forward to clean energy future},
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@article{lu2023investigation,
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doi={https://doi.org/10.1016/j.scs.2020.102052},
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  volume={14},
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@article{hassan2024knowledge,
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doi={https://doi.org/10.1016/j.bspc.2024.106246},
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@article{hassan2024enhancing,
  title={Enhancing coffee bean classification: a comparative analysis of pre-trained deep learning models},
  author={Hassan, Esraa},
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  volume={36},
  number={16},
  pages={9023--9052},
  year={2024},
doi={https://doi.org/10.1007/s00521-024-09623-z},
  publisher={Springer}
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@article{bashir2025wind,
  title={Wind and solar power forecasting based on hybrid CNN-ABiLSTM, CNN-transformer-MLP models},
  author={Bashir, Tasarruf and Wang, Huifang and Tahir, Mustafa and Zhang, Yixiang},
  journal={Renewable Energy},
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doi={https://doi.org/10.1016/j.renene.2024.122055},
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  number={2},
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doi={https://doi.org/10.21608/jaiep.2024.386693},

  publisher={The Scientific Association for Studies and Applied Research (SASAR).}
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  title={A hybrid model based on discrete wavelet transform (DWT) and bidirectional recurrent neural networks for wind speed prediction},
  author={Barjasteh, Arezoo and Ghafouri, Seyyed Hamid and Hashemi, Malihe},
  journal={Engineering Applications of Artificial Intelligence},
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  year={2024},
doi={https://doi.org/10.1016/j.engappai.2023.107340},
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  title={A hybrid deep learning model based on parallel architecture TCN-LSTM with Savitzky-Golay filter for wind power prediction},
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  year={2024},
doi={https://doi.org/10.1016/j.enconman.2024.118122},
  publisher={Elsevier}
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@article{salman2024hybrid,
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  journal={Neural Computing and Applications},
  volume={36},
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  year={2024},
doi={https://doi.org/10.1007/s00521-024-09558-5},
  publisher={Springer}
}
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  year={2024},
doi={https://doi.org/10.1007/s00202-024-02638-8},
  publisher={Springer}
}
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  organization={IEEE}
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doi={https://doi.org/10.1016/j.egyai.2021.100060},
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  title={Short-term wind speed forecasting based on two-stage preprocessing method, sparrow search algorithm and long short-term memory neural network},
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  volume={8},
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doi={https://doi.org/10.1016/j.egyr.2022.11.051},
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@article{bedewy2023estimate,
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  title={Wind energy assessment using Weibull distribution with different numerical estimation methods: a case study},
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@article{couto2023wind,
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doi={10.1016/j.jclepro.2023.138793},
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  pages={125021},
  year={2022},
doi={https://doi.org/10.1016/j.energy.2022.125021},
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  title={Deterministic and probabilistic multi-time-scale forecasting of wind speed based on secondary decomposition, DFIGR and a hybrid deep learning method},
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doi={https://doi.org/10.1016/j.eswa.2023.121051},
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  journal={Energies},
  volume={16},
  number={6},
  pages={2688},
  year={2023},
  publisher={MDPI}
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@article{qu2023d,
  title={A D-stacking dual-fusion, spatio-temporal graph deep neural network based on a multi-integrated overlay for short-term wind-farm cluster power multi-step prediction},
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  year={2023},
doi={https://doi.org/10.1016/j.energy.2023.128289},
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@article{benti2023forecasting,
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  author={Benti, Natei Ermias and Chaka, Mesfin Diro and Semie, Addisu Gezahegn},
  journal={Sustainability},
  volume={15},
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  pages={7087},
  year={2023},
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@article{peng2021integrated,
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doi={https://doi.org/10.1016/j.energy.2021.119887},
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@article{feng2022ml,
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@article{zhang2023deterministic,
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  volume={16},
  number={10},
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  year={2023},
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@article{liu2022novel,
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@article{liu2023recent,
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@article{xiong2023dual,
  title={A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction},
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  journal={Energy},
  volume={266},
  pages={126419},
  year={2023},
doi={https://doi.org/10.1016/j.energy.2022.126419},
  publisher={Elsevier}
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@article{ewees2022hbo,
  title={HBO-LSTM: Optimized long short term memory with heap-based optimizer for wind power forecasting},
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  journal={Energy Conversion and Management},
  volume={268},
  pages={116022},
doi={https://doi.org/10.1016/j.enconman.2022.116022},
  year={2022},
  publisher={Elsevier}
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@article{zhang2022short,
  title={Short-term offshore wind power forecasting-A hybrid model based on Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and deep-learning-based Long Short-Term Memory (LSTM)},
  author={Zhang, Wanqing and Lin, Zi and Liu, Xiaolei},
  journal={Renewable Energy},
  volume={185},
  pages={611--628},
  year={2022},
doi={https://doi.org/10.1016/j.renene.2021.12.100},
  publisher={Elsevier}
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@article{wang2023wind,
  title={Wind power curve modeling with large-scale generalized kernel-based regression model},
  author={Wang, Yun and Duan, Xiaocong and Song, Dongran and Zou, Runmin and Zhang, Fan and Li, Yifen},
  journal={IEEE Transactions on Sustainable Energy},
  volume={14},
  number={4},
  pages={2121--2132},
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