Explainable Reinforcement Learning-based Home Energy Management Systems using Differentiable Decision Trees

arXiv:2403.11947v1 Announce Type: cross
Abstract: With the ongoing energy transition, demand-side flexibility has become an important aspect of the modern power grid for providing grid support and allowing further integration of sustainable energy sources. Besides traditional sources, the residential sector is another major and largely untapped source of flexibility, driven by the increased adoption of solar PV, home batteries, and EVs. However, unlocking this residential flexibility is challenging as it requires a control framework that can effectively manage household energy …

abstract arxiv become cs.lg cs.sy decision decision trees demand differentiable eess.sy energy energy management flexibility grid home integration major management modern power reinforcement reinforcement learning sector support sustainable sustainable energy systems transition trees type