Explainable AI: Deep Reinforcement Learning Agents for Residential Demand Side Cost Savings in Smart Grids
Machine Learning
2019-10-31 v2 Artificial Intelligence
Computer Science and Game Theory
Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.1910.08719,
title = {Explainable AI: Deep Reinforcement Learning Agents for Residential Demand Side Cost Savings in Smart Grids},
author = {Hareesh Kumar and Priyanka Mary Mammen and Krithi Ramamritham},
journal= {arXiv preprint arXiv:1910.08719},
year = {2019}
}