A method for real-time optimal heliostat aiming strategy generation via deep learning

Abstract

Optimal aiming strategies are essential for efficient solar power tower technology operation. However, the high calculation complexity makes it difficult for existing optimization methods to solve the optimization problem in real-time directly. This work proposes a real-time optimal heliostat aiming strategy generation method via deep learning. First, a two-stage learning scheme where the neural network models are trained by genetic algorithm (GA) benchmark solutions to produce an optimal aiming strategy is presented. Then, an end-to-end model without needing GA solutions for training is developed and discussed. Furthermore, a robust end-to-end training method using randomly sampled flux maps is also proposed. The proposed models demonstrated comparable performance as GA with two orders of magnitude less computation time through case studies. Among the proposed models, the end-to-end model shows significantly better generalization ability than the pure data-driven two-stage model on the test set. A robust end-to-end model with data enhancement has better robustness on unseen flux maps.