A neural-network surrogate embedded in a mixed-integer optimization finds heliostat aiming factors that flatten receiver flux and reduce peak concentration by about 9% with only about 2% energy loss compared to a sweep heuristic.
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Leveraging Neural Networks to Optimize Heliostat Field Aiming Strategies in Concentrating Solar Power Tower Plants
A neural-network surrogate embedded in a mixed-integer optimization finds heliostat aiming factors that flatten receiver flux and reduce peak concentration by about 9% with only about 2% energy loss compared to a sweep heuristic.