Real-time heliostat field aiming strategy optimization based on reinforcement learning
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DOI: 10.1016/j.apenergy.2021.118224
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Cited by:
- Yerudkar, Aditi N. & Kumar, Durgesh & Dalvi, Vishwanath H. & Panse, Sudhir V. & Gaval, Vivek R. & Joshi, Jyeshtharaj B., 2024. "Economically feasible solutions in concentrating solar power technology specifically for heliostats – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
- Wang, Shuang & Asselineau, Charles-Alexis & Fontalvo, Armando & Wang, Ye & Logie, William & Pye, John & Coventry, Joe, 2023. "Co-optimisation of the heliostat field and receiver for concentrated solar power plants," Applied Energy, Elsevier, vol. 348(C).
- Ruidi Zhu & Dong Ni, 2023. "A Model Predictive Control Approach for Heliostat Field Power Regulatory Aiming Strategy under Varying Cloud Shadowing Conditions," Energies, MDPI, vol. 16(7), pages 1-19, March.
- Yi’an Wang & Zhe Wu & Dong Ni, 2024. "Large-Scale Optimization among Photovoltaic and Concentrated Solar Power Systems: A State-of-the-Art Review and Algorithm Analysis," Energies, MDPI, vol. 17(17), pages 1-38, August.
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Keywords
Solar thermal power; Heliostat aiming strategy; Real-time optimization; Combinatorial optimization problem; Reinforcement learning;All these keywords.
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