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Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models

Citations

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Cited by:

  1. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
  2. Nikola Mišnić & Bojan Pejović & Jelena Jovović & Sunčica Rogić & Vladimir Đurišić, 2022. "The Economic Viability of PV Power Plant Based on a Neural Network Model of Electricity Prices Forecast: A Case of a Developing Market," Energies, MDPI, vol. 15(17), pages 1-14, August.
  3. Juntao Li & Tianxu Cui & Kaiwen Yang & Ruiping Yuan & Liyan He & Mengtao Li, 2021. "Demand Forecasting of E-Commerce Enterprises Based on Horizontal Federated Learning from the Perspective of Sustainable Development," Sustainability, MDPI, vol. 13(23), pages 1-29, November.
  4. Belqasem Aljafari & Siva Rama Krishna Madeti & Priya Ranjan Satpathy & Sudhakar Babu Thanikanti & Bamidele Victor Ayodele, 2022. "Automatic Monitoring System for Online Module-Level Fault Detection in Grid-Tied Photovoltaic Plants," Energies, MDPI, vol. 15(20), pages 1-28, October.
  5. Liu, Jincheng & Li, Teng, 2024. "Multi-step power forecasting for regional photovoltaic plants based on ITDE-GAT model," Energy, Elsevier, vol. 293(C).
  6. Neethu Elizabeth Michael & Manohar Mishra & Shazia Hasan & Ahmed Al-Durra, 2022. "Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique," Energies, MDPI, vol. 15(6), pages 1-20, March.
  7. Mirza, Adeel Feroz & Mansoor, Majad & Usman, Muhammad & Ling, Qiang, 2023. "A comprehensive approach for PV wind forecasting by using a hyperparameter tuned GCVCNN-MRNN deep learning model," Energy, Elsevier, vol. 283(C).
  8. Negri, Simone & Giani, Federico & Blasuttigh, Nicola & Massi Pavan, Alessandro & Mellit, Adel & Tironi, Enrico, 2022. "Combined model predictive control and ANN-based forecasters for jointly acting renewable self-consumers: An environmental and economical evaluation," Renewable Energy, Elsevier, vol. 198(C), pages 440-454.
  9. Xue-Bo Jin & Wen-Tao Gong & Jian-Lei Kong & Yu-Ting Bai & Ting-Li Su, 2022. "PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data," Mathematics, MDPI, vol. 10(4), pages 1-17, February.
  10. Huang, Songtao & Zhou, Qingguo & Shen, Jun & Zhou, Heng & Yong, Binbin, 2024. "Multistage spatio-temporal attention network based on NODE for short-term PV power forecasting," Energy, Elsevier, vol. 290(C).
  11. Xu, Shaozhen & Liu, Jun & Huang, Xiaoqiao & Li, Chengli & Chen, Zaiqing & Tai, Yonghang, 2024. "Minutely multi-step irradiance forecasting based on all-sky images using LSTM-InformerStack hybrid model with dual feature enhancement," Renewable Energy, Elsevier, vol. 224(C).
  12. 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).
  13. 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).
  14. Liu, Xingdou & Zhang, Li & Wang, Jiangong & Zhou, Yue & Gan, Wei, 2023. "A unified multi-step wind speed forecasting framework based on numerical weather prediction grids and wind farm monitoring data," Renewable Energy, Elsevier, vol. 211(C), pages 948-963.
  15. Zheng, Lingwei & Su, Ran & Sun, Xinyu & Guo, Siqi, 2023. "Historical PV-output characteristic extraction based weather-type classification strategy and its forecasting method for the day-ahead prediction of PV output," Energy, Elsevier, vol. 271(C).
  16. Sabadus, Andreea & Blaga, Robert & Hategan, Sergiu-Mihai & Calinoiu, Delia & Paulescu, Eugenia & Mares, Oana & Boata, Remus & Stefu, Nicoleta & Paulescu, Marius & Badescu, Viorel, 2024. "A cross-sectional survey of deterministic PV power forecasting: Progress and limitations in current approaches," Renewable Energy, Elsevier, vol. 226(C).
  17. Moradzadeh, Arash & Moayyed, Hamed & Mohammadi-Ivatloo, Behnam & Vale, Zita & Ramos, Carlos & Ghorbani, Reza, 2023. "A novel cyber-Resilient solar power forecasting model based on secure federated deep learning and data visualization," Renewable Energy, Elsevier, vol. 211(C), pages 697-705.
  18. Robert Basmadjian & Amirhossein Shaafieyoun, 2023. "Assessing ARIMA-Based Forecasts for the Percentage of Renewables in Germany: Insights and Lessons for the Future," Energies, MDPI, vol. 16(16), pages 1-19, August.
  19. Zhang, Chu & Ji, Chunlei & Hua, Lei & Ma, Huixin & Nazir, Muhammad Shahzad & Peng, Tian, 2022. "Evolutionary quantile regression gated recurrent unit network based on variational mode decomposition, improved whale optimization algorithm for probabilistic short-term wind speed prediction," Renewable Energy, Elsevier, vol. 197(C), pages 668-682.
  20. Ding, Jiaqi & Zhao, Pu & Liu, Changjun & Wang, Xiaofang & Xie, Rong & Liu, Haitao, 2024. "From irregular to continuous: The deep Koopman model for time series forecasting of energy equipment," Applied Energy, Elsevier, vol. 364(C).
  21. Luo, Xing & Zhang, Dongxiao, 2023. "A cascaded deep learning framework for photovoltaic power forecasting with multi-fidelity inputs," Energy, Elsevier, vol. 268(C).
  22. Wang, Jianzhou & Yu, Yue & Zeng, Bo & Lu, Haiyan, 2024. "Hybrid ultra-short-term PV power forecasting system for deterministic forecasting and uncertainty analysis," Energy, Elsevier, vol. 288(C).
  23. Dušan P. Nikezić & Uzahir R. Ramadani & Dušan S. Radivojević & Ivan M. Lazović & Nikola S. Mirkov, 2022. "Deep Learning Model for Global Spatio-Temporal Image Prediction," Mathematics, MDPI, vol. 10(18), pages 1-15, September.
  24. Li, Xuan & Zhang, Wei, 2022. "Physics-informed deep learning model in wind turbine response prediction," Renewable Energy, Elsevier, vol. 185(C), pages 932-944.
  25. Hu, Yusha & Man, Yi, 2023. "Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
  26. 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).
  27. Zhang, Weiyi & Zhou, Haiyang & Bao, Xiaohua & Cui, Hongzhi, 2023. "Outlet water temperature prediction of energy pile based on spatial-temporal feature extraction through CNN–LSTM hybrid model," Energy, Elsevier, vol. 264(C).
  28. 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).
  29. Xiaohan Huang & Aihua Jiang, 2022. "Wind Power Generation Forecast Based on Multi-Step Informer Network," Energies, MDPI, vol. 15(18), pages 1-17, September.
  30. Han, Yan & Mi, Lihua & Shen, Lian & Cai, C.S. & Liu, Yuchen & Li, Kai & Xu, Guoji, 2022. "A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting," Applied Energy, Elsevier, vol. 312(C).
  31. Niu, Yunbo & Wang, Jianzhou & Zhang, Ziyuan & Luo, Tianrui & Liu, Jingjiang, 2024. "De-Trend First, Attend Next: A Mid-Term PV forecasting system with attention mechanism and encoder–decoder structure," Applied Energy, Elsevier, vol. 353(PB).
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