Explainable AI: Deep Reinforcement Learning Agents for Residential Demand Side Cost Savings in Smart Grids
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Motivated by the recent advancements in deep Reinforcement Learning (RL), we develop an RL agent to manage the operation of storage devices in a household designed to maximize demand-side cost savings. The proposed technique is data-driven, and the RL agent learns from scratch on how to efficiently use the energy storage device under variable tariff-structures Contracting the concept of the "black box" where the techniques learned by the agent are ignored. We explain the learning progression of the RL agent, and the strategies it follows based on the capacity of the storage device.
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