Thermal power forecasting of solar power tower system by combining mechanism modeling and deep learning method
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DOI: 10.1016/j.energy.2020.118403
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Cited by:
- Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
- Wang, Chen & Guo, Su & Pei, Huanjin & He, Yi & Liu, Deyou & Li, Mengying, 2023. "Rolling optimization based on holism for the operation strategy of solar tower power plant," Applied Energy, Elsevier, vol. 331(C).
- Su, Zixiang & Yang, Liu & Wang, Hao & Song, Jianzhong & Jiang, Weixue, 2024. "Exergoenvironmental optimization and thermoeconomic assessment of an innovative multistage Brayton cycle with dual expansion and cooling for ultra-high temperature solar power," Energy, Elsevier, vol. 286(C).
- Zheng, Jianqin & Du, Jian & Wang, Bohong & Klemeš, Jiří Jaromír & Liao, Qi & Liang, Yongtu, 2023. "A hybrid framework for forecasting power generation of multiple renewable energy sources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
- Hou, Guolian & Fan, Yuzhen & Wang, Junjie, 2024. "Application of a novel dynamic recurrent fuzzy neural network with rule self-adaptation based on chaotic quantum pigeon-inspired optimization in modeling for gas turbine," Energy, Elsevier, vol. 290(C).
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Keywords
Solar power tower; Thermal power forecasting; Mechanism modeling; Deep learning; Major meteorological factors; Spatial-temporal features;All these keywords.
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