Distill2Explain: Differentiable decision trees for explainable reinforcement learning in energy application controllers

arXiv:2403.11907v1 Announce Type: cross
Abstract: Demand-side flexibility is gaining importance as a crucial element in the energy transition process. Accounting for about 25% of final energy consumption globally, the residential sector is an important (potential) source of energy flexibility. However, unlocking this flexibility requires developing a control framework that (1) easily scales across different houses, (2) is easy to maintain, and (3) is simple to understand for end-users. A potential control framework for such a task is data-driven control, specifically …

abstract accounting application arxiv consumption control cs.lg cs.sy decision decision trees demand differentiable eess.sy element energy flexibility framework however importance process reinforcement reinforcement learning sector transition trees type