Prospective Experiment for Reinforcement Learning on Demand Response in a Social Game Framework

e-Energy '20: The Eleventh ACM International Conference on Future Energy Systems Virtual Event Australia June, 2020(2020)

引用 7|浏览27
暂无评分
摘要
Improving demand response can help optimize renewable energy use and might be possible using current tools in machine learning. We propose an experiment to test the development of Reinforcement Learning (RL) agents to learn to vary a daily grid price signal to optimize behavioral energy shift in office workers. We describe our application of Batch Constrained Q Learning and Soft Actor Critic (SAC) as RL agents and Social Cognitive Theory, LSTM networks, and linear regression as planning models. We report limited success within simulation with SAC and linear regression. Finally, we propose an experiment timeline for consideration.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要