Explainable Reinforcement Learning-based Home Energy Management Systems
  using Differentiable Decision Trees
Gargya Gokhale Bert Claessens Chris Develder
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
consumption, and maintain user comfort while being readily scalable across
different, diverse houses. We aim to address this challenging problem and
introduce a reinforcement learning-based approach using differentiable decision
trees. This approach integrates the scalability of data-driven reinforcement
learning with the explainability of (differentiable) decision trees. This leads
to a controller that can be easily adapted across different houses and provides
a simple control policy that can be explained to end-users, further improving
user acceptance. As a proof-of-concept, we analyze our method using a home
energy management problem, comparing its performance with commercially
available rule-based baseline and standard neural network-based RL controllers.
Through this preliminary study, we show that the performance of our proposed
method is comparable to standard RL-based controllers, outperforming baseline
controllers by ~20% in terms of daily cost savings while being straightforward
to explain.