IDEAS home Printed from https://ideas.repec.org/r/eee/energy/v231y2021ics0360544221011567.html
   My bibliography  Save this item

Photovoltaic power forecasting based on GA improved Bi-LSTM in microgrid without meteorological information

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Sheng Huang & Huakun Que & Lukun Zeng & Jingxu Yang & Kaihong Zheng, 2024. "Multi-Scale Graph Attention Network Based on Encoding Decomposition for Electricity Consumption Prediction," Energies, MDPI, vol. 17(23), pages 1-18, November.
  2. Yubo Wang & Chao Huo & Fei Xu & Libin Zheng & Ling Hao, 2025. "Ultra-Short-Term Distributed Photovoltaic Power Probabilistic Forecasting Method Based on Federated Learning and Joint Probability Distribution Modeling," Energies, MDPI, vol. 18(1), pages 1-21, January.
  3. Zhang, Yagang & Wang, Hui & Wang, Jingchao & Cheng, Xiaodan & Wang, Tong & Zhao, Zheng, 2024. "Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system," Energy, Elsevier, vol. 292(C).
  4. Shasha Yang & Anjie Jin & Wen Nie & Cong Liu & Yu Li, 2022. "Research on SSA-LSTM-Based Slope Monitoring and Early Warning Model," Sustainability, MDPI, vol. 14(16), pages 1-16, August.
  5. Wang, Yuqing & Fu, Wenjie & Wang, Junlong & Zhen, Zhao & Wang, Fei, 2024. "Ultra-short-term distributed PV power forecasting for virtual power plant considering data-scarce scenarios," Applied Energy, Elsevier, vol. 373(C).
  6. Lai, Wenzhe & Zhen, Zhao & Wang, Fei & Fu, Wenjie & Wang, Junlong & Zhang, Xudong & Ren, Hui, 2024. "Sub-region division based short-term regional distributed PV power forecasting method considering spatio-temporal correlations," Energy, Elsevier, vol. 288(C).
  7. Huang, Congzhi & Yang, Mengyuan, 2023. "Memory long and short term time series network for ultra-short-term photovoltaic power forecasting," Energy, Elsevier, vol. 279(C).
  8. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
  9. Hui Wang & Su Yan & Danyang Ju & Nan Ma & Jun Fang & Song Wang & Haijun Li & Tianyu Zhang & Yipeng Xie & Jun Wang, 2023. "Short-Term Photovoltaic Power Forecasting Based on a Feature Rise-Dimensional Two-Layer Ensemble Learning Model," Sustainability, MDPI, vol. 15(21), pages 1-26, November.
  10. Liu, Jincheng & Li, Teng, 2024. "Multi-step power forecasting for regional photovoltaic plants based on ITDE-GAT model," Energy, Elsevier, vol. 293(C).
  11. Hong Wu & Haipeng Liu & Huaiping Jin & Yanping He, 2024. "Ultra-Short-Term Photovoltaic Power Prediction by NRGA-BiLSTM Considering Seasonality and Periodicity of Data," Energies, MDPI, vol. 17(18), pages 1-19, September.
  12. Yang, Shaomei & Luo, Yuman, 2025. "Short-term photovoltaic power prediction based on RF-SGMD-GWO-BiLSTM hybrid models," Energy, Elsevier, vol. 316(C).
  13. 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.
  14. Shabbir, Noman & Kütt, Lauri & Astapov, Victor & Daniel, Kamran & Jawad, Muhammad & Husev, Oleksandr & Rosin, Argo & Martins, João, 2024. "Enhancing PV hosting capacity and mitigating congestion in distribution networks with deep learning based PV forecasting and battery management," Applied Energy, Elsevier, vol. 372(C).
  15. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2022. "Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
  16. Gao, Yuan & Hu, Zehuan & Shi, Shanrui & Chen, Wei-An & Liu, Mingzhe, 2024. "Adversarial discriminative domain adaptation for solar radiation prediction: A cross-regional study for zero-label transfer learning in Japan," Applied Energy, Elsevier, vol. 359(C).
  17. Jiahui Wang & Mingsheng Jia & Shishi Li & Kang Chen & Cheng Zhang & Xiuyu Song & Qianxi Zhang, 2024. "Short-Term Power-Generation Prediction of High Humidity Island Photovoltaic Power Station Based on a Deep Hybrid Model," Sustainability, MDPI, vol. 16(7), pages 1-24, March.
  18. Cao, Yisheng & Liu, Gang & Luo, Donghua & Bavirisetti, Durga Prasad & Xiao, Gang, 2023. "Multi-timescale photovoltaic power forecasting using an improved Stacking ensemble algorithm based LSTM-Informer model," Energy, Elsevier, vol. 283(C).
  19. Sameer Al-Dahidi & Manoharan Madhiarasan & Loiy Al-Ghussain & Ahmad M. Abubaker & Adnan Darwish Ahmad & Mohammad Alrbai & Mohammadreza Aghaei & Hussein Alahmer & Ali Alahmer & Piero Baraldi & Enrico Z, 2024. "Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework," Energies, MDPI, vol. 17(16), pages 1-38, August.
  20. Chen, Xiang & Ding, Kun & Zhang, Jingwei & Han, Wei & Liu, Yongjie & Yang, Zenan & Weng, Shuai, 2022. "Online prediction of ultra-short-term photovoltaic power using chaotic characteristic analysis, improved PSO and KELM," Energy, Elsevier, vol. 248(C).
  21. Arooj, Qumrish, 2024. "FedWindT: Federated learning assisted transformer architecture for collaborative and secure wind power forecasting in diverse conditions," Energy, Elsevier, vol. 309(C).
  22. Lin, Huapeng & Gao, Liyuan & Cui, Mingtao & Liu, Hengchao & Li, Chunyang & Yu, Miao, 2025. "Short-term distributed photovoltaic power prediction based on temporal self-attention mechanism and advanced signal decomposition techniques with feature fusion," Energy, Elsevier, vol. 315(C).
  23. Hu, Deng & Wang, Hechun & Yang, Chuanlei & Wang, Binbin & Duan, Baoyin & Wang, Yinyan & Li, Hucai, 2024. "Construction of digital twin model of engine in-cylinder combustion based on data-driven," Energy, Elsevier, vol. 293(C).
  24. Linh Bui Duy & Ninh Nguyen Quang & Binh Doan Van & Eleonora Riva Sanseverino & Quynh Tran Thi Tu & Hang Le Thi Thuy & Sang Le Quang & Thinh Le Cong & Huyen Cu Thi Thanh, 2024. "Refining Long Short-Term Memory Neural Network Input Parameters for Enhanced Solar Power Forecasting," Energies, MDPI, vol. 17(16), pages 1-22, August.
  25. Keyong Hu & Zheyi Fu & Chunyuan Lang & Wenjuan Li & Qin Tao & Ben Wang, 2024. "Short-Term Photovoltaic Power Generation Prediction Based on Copula Function and CNN-CosAttention-Transformer," Sustainability, MDPI, vol. 16(14), pages 1-18, July.
  26. Fahad Radhi Alharbi & Denes Csala, 2021. "Wind Speed and Solar Irradiance Prediction Using a Bidirectional Long Short-Term Memory Model Based on Neural Networks," Energies, MDPI, vol. 14(20), pages 1-22, October.
  27. liu, Qian & li, Yulin & jiang, Hang & chen, Yilin & zhang, Jiang, 2024. "Short-term photovoltaic power forecasting based on multiple mode decomposition and parallel bidirectional long short term combined with convolutional neural networks," Energy, Elsevier, vol. 286(C).
  28. Shabbir, Noman & Kütt, Lauri & Raja, Hadi A. & Jawad, Muhammad & Allik, Alo & Husev, Oleksandr, 2022. "Techno-economic analysis and energy forecasting study of domestic and commercial photovoltaic system installations in Estonia," Energy, Elsevier, vol. 253(C).
  29. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Liu, Jun & Shi, Junsheng & Liu, Wuming, 2022. "Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM," Energy, Elsevier, vol. 246(C).
  30. Wu, Thomas & Hu, Ruifeng & Zhu, Hongyu & Jiang, Meihui & Lv, Kunye & Dong, Yunxuan & Zhang, Dongdong, 2024. "Combined IXGBoost-KELM short-term photovoltaic power prediction model based on multidimensional similar day clustering and dual decomposition," Energy, Elsevier, vol. 288(C).
  31. Gao, Yuan & Hu, Zehuan & Chen, Wei-An & Liu, Mingzhe, 2024. "Solutions to the insufficiency of label data in renewable energy forecasting: A comparative and integrative analysis of domain adaptation and fine-tuning," Energy, Elsevier, vol. 302(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.