Explainable AI: Deep Reinforcement Learning Agents for Residential Demand Side Cost Savings in Smart Grids
This work addresses cost savings for residential users in smart grids, but it is incremental as it focuses on explaining the learning process of an existing RL method.
The paper tackles the problem of managing household energy storage to maximize cost savings in smart grids using a deep reinforcement learning agent, achieving efficient operation under variable tariffs through data-driven learning from scratch.
Motivated by recent advancements in Deep Reinforcement Learning (RL), we have developed an RL agent to manage the operation of storage devices in a household and is designed to maximize demand-side cost savings. The proposed technique is data-driven, and the RL agent learns from scratch how to efficiently use the energy storage device given variable tariff structures. In most of the studies, the RL agent is considered as a black box, and how the agent has learned is often ignored. We explain the learning progression of the RL agent, and the strategies it follows based on the capacity of the storage device.