Reinforcement learning-enhanced genetic algorithm for wind farm layout optimization

no code implementations • 24 Nov 2024 • Guodan Dong, Jianhua Qin, Chutian Wu, Chang Xu, Xiaolei Yang

To illustrate the accuracy and efficiency of the proposed RLGA, we evaluate the WFLO problem for four layouts (aligned, staggered, sunflower, and unstructured) under unidirectional uniform wind, comparing the results with those from the GA. RLGA achieves similar results to GA for aligned and staggered layouts and outperforms GA for sunflower and unstructured layouts, demonstrating its efficiency.

reinforcement-learning Reinforcement Learning +1