Differentiable Hybrid-Action Neural Feedback Control for District Heating Networks
This research proposes a Hybrid-Action Neural Controller (HANC) to manage complex district heating networks by jointly generating continuous commands and discrete operational decisions. The framework uses differentiable relaxations, like Gumbel-Softmax, to allow the entire hybrid policy to be trained end-to-end. The resulting controller achieves a 30% reduction in operating costs compared to rule-based systems while ensuring physical constraints are met by construction.