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Wind power forecasting using attention-based gated recurrent unit network

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Cited by:

  1. Paweł Piotrowski & Dariusz Baczyński & Marcin Kopyt & Tomasz Gulczyński, 2022. "Advanced Ensemble Methods Using Machine Learning and Deep Learning for One-Day-Ahead Forecasts of Electric Energy Production in Wind Farms," Energies, MDPI, vol. 15(4), pages 1-30, February.
  2. Bentsen, Lars Ødegaard & Warakagoda, Narada Dilp & Stenbro, Roy & Engelstad, Paal, 2023. "Spatio-temporal wind speed forecasting using graph networks and novel Transformer architectures," Applied Energy, Elsevier, vol. 333(C).
  3. Shen, Yuewen & Wen, Lihong & Shen, Chaowen, 2024. "Based on hypernetworks and multifractals: Deep distribution feature fusion for multidimensional nonstationary time series prediction," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
  4. Meng, Anbo & Wang, Peng & Zhai, Guangsong & Zeng, Cong & Chen, Shun & Yang, Xiaoyi & Yin, Hao, 2022. "Electricity price forecasting with high penetration of renewable energy using attention-based LSTM network trained by crisscross optimization," Energy, Elsevier, vol. 254(PA).
  5. Jieyun Zheng & Linyao Zhang & Jinpeng Chen & Guilian Wu & Shiyuan Ni & Zhijian Hu & Changhong Weng & Zhi Chen, 2021. "Multiple-Load Forecasting for Integrated Energy System Based on Copula-DBiLSTM," Energies, MDPI, vol. 14(8), pages 1-14, April.
  6. Chengqing, Yu & Guangxi, Yan & Chengming, Yu & Yu, Zhang & Xiwei, Mi, 2023. "A multi-factor driven spatiotemporal wind power prediction model based on ensemble deep graph attention reinforcement learning networks," Energy, Elsevier, vol. 263(PE).
  7. Guanjun Liu & Chao Wang & Hui Qin & Jialong Fu & Qin Shen, 2022. "A Novel Hybrid Machine Learning Model for Wind Speed Probabilistic Forecasting," Energies, MDPI, vol. 15(19), pages 1-16, September.
  8. Qiu, Hong & Shi, Kaikai & Wang, Renfang & Zhang, Liang & Liu, Xiufeng & Cheng, Xu, 2024. "A novel temporal–spatial graph neural network for wind power forecasting considering blockage effects," Renewable Energy, Elsevier, vol. 227(C).
  9. Li, Jingmiao & Wang, Jun, 2020. "Forcasting of energy futures market and synchronization based on stochastic gated recurrent unit model," Energy, Elsevier, vol. 213(C).
  10. Nascimento, Erick Giovani Sperandio & de Melo, Talison A.C. & Moreira, Davidson M., 2023. "A transformer-based deep neural network with wavelet transform for forecasting wind speed and wind energy," Energy, Elsevier, vol. 278(C).
  11. Saeed, Adnan & Li, Chaoshun & Gan, Zhenhao & Xie, Yuying & Liu, Fangjie, 2022. "A simple approach for short-term wind speed interval prediction based on independently recurrent neural networks and error probability distribution," Energy, Elsevier, vol. 238(PC).
  12. de Azevedo Takara, Lucas & Teixeira, Ana Clara & Yazdanpanah, Hamed & Mariani, Viviana Cocco & dos Santos Coelho, Leandro, 2024. "Optimizing multi-step wind power forecasting: Integrating advanced deep neural networks with stacking-based probabilistic learning," Applied Energy, Elsevier, vol. 369(C).
  13. Dai, Xiaoran & Liu, Guo-Ping & Hu, Wenshan, 2023. "An online-learning-enabled self-attention-based model for ultra-short-term wind power forecasting," Energy, Elsevier, vol. 272(C).
  14. Leng, Chunyang & Jia, Mingxing & Zheng, Haijin & Deng, Jibin & Niu, Dapeng, 2023. "Dynamic liquid level prediction in oil wells during oil extraction based on WOA-AM-LSTM-ANN model using dynamic and static information," Energy, Elsevier, vol. 282(C).
  15. Liu, Lei & Liu, Jicheng & Ye, Yu & Liu, Hui & Chen, Kun & Li, Dong & Dong, Xue & Sun, Mingzhai, 2023. "Ultra-short-term wind power forecasting based on deep Bayesian model with uncertainty," Renewable Energy, Elsevier, vol. 205(C), pages 598-607.
  16. Han Peng & Songyin Li & Linjian Shangguan & Yisa Fan & Hai Zhang, 2023. "Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research," Sustainability, MDPI, vol. 15(10), pages 1-35, May.
  17. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2024. "Wind power forecasting: A temporal domain generalization approach incorporating hybrid model and adversarial relationship-based training," Applied Energy, Elsevier, vol. 355(C).
  18. Yu, Binbin & Li, Jianjing & Liu, Che & Sun, Bo, 2022. "A novel short-term electrical load forecasting framework with intelligent feature engineering," Applied Energy, Elsevier, vol. 327(C).
  19. Zang, Haixiang & Xu, Ruiqi & Cheng, Lilin & Ding, Tao & Liu, Ling & Wei, Zhinong & Sun, Guoqiang, 2021. "Residential load forecasting based on LSTM fusing self-attention mechanism with pooling," Energy, Elsevier, vol. 229(C).
  20. Paweł Piotrowski & Inajara Rutyna & Dariusz Baczyński & Marcin Kopyt, 2022. "Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors," Energies, MDPI, vol. 15(24), pages 1-38, December.
  21. Zhang, Ziyuan & Wang, Jianzhou & Wei, Danxiang & Luo, Tianrui & Xia, Yurui, 2023. "A novel ensemble system for short-term wind speed forecasting based on Two-stage Attention-Based Recurrent Neural Network," Renewable Energy, Elsevier, vol. 204(C), pages 11-23.
  22. Chen, Hang & Wei, Shanbi & Yang, Wei & Liu, Shanchao, 2023. "Input wind speed forecasting for wind turbines based on spatio-temporal correlation," Renewable Energy, Elsevier, vol. 216(C).
  23. Dokur, Emrah & Erdogan, Nuh & Salari, Mahdi Ebrahimi & Karakuzu, Cihan & Murphy, Jimmy, 2022. "Offshore wind speed short-term forecasting based on a hybrid method: Swarm decomposition and meta-extreme learning machine," Energy, Elsevier, vol. 248(C).
  24. Zhao, Beizhen & He, Xin & Ran, Shaolin & Zhang, Yong & Cheng, Cheng, 2024. "Spatial correlation learning based on graph neural network for medium-term wind power forecasting," Energy, Elsevier, vol. 296(C).
  25. Liu, Chenyu & Zhang, Xuemin & Mei, Shengwei & Zhou, Qingyu & Fan, Hang, 2023. "Series-wise attention network for wind power forecasting considering temporal lag of numerical weather prediction," Applied Energy, Elsevier, vol. 336(C).
  26. Yan, Jie & Möhrlen, Corinna & Göçmen, Tuhfe & Kelly, Mark & Wessel, Arne & Giebel, Gregor, 2022. "Uncovering wind power forecasting uncertainty sources and their propagation through the whole modelling chain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
  27. Pamir & Nadeem Javaid & Saher Javaid & Muhammad Asif & Muhammad Umar Javed & Adamu Sani Yahaya & Sheraz Aslam, 2022. "Synthetic Theft Attacks and Long Short Term Memory-Based Preprocessing for Electricity Theft Detection Using Gated Recurrent Unit," Energies, MDPI, vol. 15(8), pages 1-20, April.
  28. Tang, Yugui & Yang, Kuo & Zheng, Yichu & Ma, Li & Zhang, Shujing & Zhang, Zhen, 2024. "Wind power forecasting: A transfer learning approach incorporating temporal convolution and adversarial training," Renewable Energy, Elsevier, vol. 224(C).
  29. Feng, Zhong-kai & Huang, Qing-qing & Niu, Wen-jing & Yang, Tao & Wang, Jia-yang & Wen, Shi-ping, 2022. "Multi-step-ahead solar output time series prediction with gate recurrent unit neural network using data decomposition and cooperation search algorithm," Energy, Elsevier, vol. 261(PA).
  30. Peng, Cheng & Zhang, Yiqin & Zhang, Bowen & Song, Dan & Lyu, Yi & Tsoi, AhChung, 2023. "A novel ultra-short-term wind power prediction method based on XA mechanism," Applied Energy, Elsevier, vol. 351(C).
  31. Wang, Shuai & Li, Bin & Li, Guanzheng & Yao, Bin & Wu, Jianzhong, 2021. "Short-term wind power prediction based on multidimensional data cleaning and feature reconfiguration," Applied Energy, Elsevier, vol. 292(C).
  32. 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).
  33. Luca Di Persio & Nicola Fraccarolo & Andrea Veronese, 2024. "Wind Energy Production in Italy: A Forecasting Approach Based on Fractional Brownian Motion and Generative Adversarial Networks," Mathematics, MDPI, vol. 12(13), pages 1-16, July.
  34. Liu, Hongyi & Han, Hua & Sun, Yao & Shi, Guangze & Su, Mei & Liu, Zhangjie & Wang, Hongfei & Deng, Xiaofei, 2022. "Short-term wind power interval prediction method using VMD-RFG and Att-GRU," Energy, Elsevier, vol. 251(C).
  35. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
  36. Yun, Eunjeong & Hur, Jin, 2021. "Probabilistic estimation model of power curve to enhance power output forecasting of wind generating resources," Energy, Elsevier, vol. 223(C).
  37. Yang, Ting & Yang, Zhenning & Li, Fei & Wang, Hengyu, 2024. "A short-term wind power forecasting method based on multivariate signal decomposition and variable selection," Applied Energy, Elsevier, vol. 360(C).
  38. Zucatelli, P.J. & Nascimento, E.G.S. & Santos, A.Á.B. & Arce, A.M.G. & Moreira, D.M., 2021. "An investigation on deep learning and wavelet transform to nowcast wind power and wind power ramp: A case study in Brazil and Uruguay," Energy, Elsevier, vol. 230(C).
  39. Lv, Sheng-Xiang & Wang, Lin, 2022. "Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization," Applied Energy, Elsevier, vol. 311(C).
  40. Ahmad, Tanveer & Zhang, Dongdong, 2022. "A data-driven deep sequence-to-sequence long-short memory method along with a gated recurrent neural network for wind power forecasting," Energy, Elsevier, vol. 239(PB).
  41. Jing Wan & Jiehui Huang & Zhiyuan Liao & Chunquan Li & Peter X. Liu, 2022. "A Multi-View Ensemble Width-Depth Neural Network for Short-Term Wind Power Forecasting," Mathematics, MDPI, vol. 10(11), pages 1-20, May.
  42. Wang, Lin & Tao, Rui & Hu, Huanling & Zeng, Yu-Rong, 2021. "Effective wind power prediction using novel deep learning network: Stacked independently recurrent autoencoder," Renewable Energy, Elsevier, vol. 164(C), pages 642-655.
  43. 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).
  44. Ling Liu & Fang Liu & Yuling Zheng, 2021. "A Novel Ultra-Short-Term PV Power Forecasting Method Based on DBN-Based Takagi-Sugeno Fuzzy Model," Energies, MDPI, vol. 14(20), pages 1-10, October.
  45. Meng, Anbo & Chen, Shu & Ou, Zuhong & Xiao, Jianhua & Zhang, Jianfeng & Chen, Shun & Zhang, Zheng & Liang, Ruduo & Zhang, Zhan & Xian, Zikang & Wang, Chenen & Yin, Hao & Yan, Baiping, 2022. "A novel few-shot learning approach for wind power prediction applying secondary evolutionary generative adversarial network," Energy, Elsevier, vol. 261(PA).
  46. Fachrizal Aksan & Yang Li & Vishnu Suresh & Przemysław Janik, 2023. "Multistep Forecasting of Power Flow Based on LSTM Autoencoder: A Study Case in Regional Grid Cluster Proposal," Energies, MDPI, vol. 16(13), pages 1-20, June.
  47. Meng, Anbo & Chen, Shun & Ou, Zuhong & Ding, Weifeng & Zhou, Huaming & Fan, Jingmin & Yin, Hao, 2022. "A hybrid deep learning architecture for wind power prediction based on bi-attention mechanism and crisscross optimization," Energy, Elsevier, vol. 238(PB).
  48. Rita Teixeira & Adelaide Cerveira & Eduardo J. Solteiro Pires & José Baptista, 2024. "Advancing Renewable Energy Forecasting: A Comprehensive Review of Renewable Energy Forecasting Methods," Energies, MDPI, vol. 17(14), pages 1-30, July.
  49. Shengxiang Lv & Lin Wang & Sirui Wang, 2023. "A Hybrid Neural Network Model for Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 16(4), pages 1-18, February.
  50. Kirchner-Bossi, Nicolas & Kathari, Gabriel & Porté-Agel, Fernando, 2024. "A hybrid physics-based and data-driven model for intra-day and day-ahead wind power forecasting considering a drastically expanded predictor search space," Applied Energy, Elsevier, vol. 367(C).
  51. Lv, Sheng-Xiang & Wang, Lin, 2023. "Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model," Energy, Elsevier, vol. 263(PE).
  52. Wang, Jianguo & Han, Lincheng & Zhang, Xiuyu & Wang, Yingzhou & Zhang, Shude, 2023. "Electrical load forecasting based on variable T-distribution and dual attention mechanism," Energy, Elsevier, vol. 283(C).
  53. Niu, Hongli & Xu, Kunliang & Liu, Cheng, 2021. "A decomposition-ensemble model with regrouping method and attention-based gated recurrent unit network for energy price prediction," Energy, Elsevier, vol. 231(C).
  54. Renxi Gong & Xianglong Li, 2023. "A Short-Term Load Forecasting Model Based on Crisscross Grey Wolf Optimizer and Dual-Stage Attention Mechanism," Energies, MDPI, vol. 16(6), pages 1-24, March.
  55. 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).
  56. Du, Pei & Yang, Dongchuan & Li, Yanzhao & Wang, Jianzhou, 2024. "An innovative interpretable combined learning model for wind speed forecasting," Applied Energy, Elsevier, vol. 358(C).
  57. Hu, Jianming & Luo, Qingxi & Tang, Jingwei & Heng, Jiani & Deng, Yuwen, 2022. "Conformalized temporal convolutional quantile regression networks for wind power interval forecasting," Energy, Elsevier, vol. 248(C).
  58. Lu, Xiaoxing & Li, Kangping & Xu, Hanchen & Wang, Fei & Zhou, Zhenyu & Zhang, Yagang, 2020. "Fundamentals and business model for resource aggregator of demand response in electricity markets," Energy, Elsevier, vol. 204(C).
  59. Zhu, Nanyang & Wang, Ying & Yuan, Kun & Yan, Jiahao & Li, Yaping & Zhang, Kaifeng, 2024. "GGNet: A novel graph structure for power forecasting in renewable power plants considering temporal lead-lag correlations," Applied Energy, Elsevier, vol. 364(C).
  60. Liu, Xin & Yang, Luoxiao & Zhang, Zijun, 2022. "The attention-assisted ordinary differential equation networks for short-term probabilistic wind power predictions," Applied Energy, Elsevier, vol. 324(C).
  61. Liu, Guanjun & Wang, Yun & Qin, Hui & Shen, Keyan & Liu, Shuai & Shen, Qin & Qu, Yuhua & Zhou, Jianzhong, 2023. "Probabilistic spatiotemporal forecasting of wind speed based on multi-network deep ensembles method," Renewable Energy, Elsevier, vol. 209(C), pages 231-247.
  62. Huang, Hao-Hsuan & Huang, Yun-Hsun, 2024. "Applying green learning to regional wind power prediction and fluctuation risk assessment," Energy, Elsevier, vol. 295(C).
  63. Al-qaness, Mohammed A.A. & Ewees, Ahmed A. & Fan, Hong & Abualigah, Laith & Elaziz, Mohamed Abd, 2022. "Boosted ANFIS model using augmented marine predator algorithm with mutation operators for wind power forecasting," Applied Energy, Elsevier, vol. 314(C).
  64. Farah, Shahid & David A, Wood & Humaira, Nisar & Aneela, Zameer & Steffen, Eger, 2022. "Short-term multi-hour ahead country-wide wind power prediction for Germany using gated recurrent unit deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
  65. Sun, zexian & Zhao, mingyu & Dong, yan & Cao, xin & Sun, Hexu, 2021. "Hybrid model with secondary decomposition, randomforest algorithm, clustering analysis and long short memory network principal computing for short-term wind power forecasting on multiple scales," Energy, Elsevier, vol. 221(C).
  66. Wu, Qiang & Zheng, Hongling & Guo, Xiaozhu & Liu, Guangqiang, 2022. "Promoting wind energy for sustainable development by precise wind speed prediction based on graph neural networks," Renewable Energy, Elsevier, vol. 199(C), pages 977-992.
  67. Wang, Huaqing & Tan, Zhongfu & Liang, Yan & Li, Fanqi & Zhang, Zheyu & Ju, Liwei, 2024. "A novel multi-layer stacking ensemble wind power prediction model under Tensorflow deep learning framework considering feature enhancement and data hierarchy processing," Energy, Elsevier, vol. 286(C).
  68. Ma, Yixiang & Yu, Lean & Zhang, Guoxing, 2022. "Short-term wind power forecasting with an intermittency-trait-driven methodology," Renewable Energy, Elsevier, vol. 198(C), pages 872-883.
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