Optimal Demand Response Using Device Based Reinforcement Learning
This addresses the need for efficient energy savings in sectors accounting for up to 65% of demand response potential, though it appears incremental as it builds on existing reinforcement learning methods.
The paper tackles the problem of automating demand response in residential and small commercial buildings by proposing a novel Energy Management System formulation based on reinforcement learning, which decomposes the problem over device clusters and achieves computational complexity linear in the number of devices.
Demand response (DR) for residential and small commercial buildings is estimated to account for as much as 65% of the total energy savings potential of DR, and previous work shows that a fully automated Energy Management System (EMS) is a necessary prerequisite to DR in these areas. In this paper, we propose a novel EMS formulation for DR problems in these sectors. Specifically, we formulate a fully automated EMS's rescheduling problem as a reinforcement learning (RL) problem, and argue that this RL problem can be approximately solved by decomposing it over device clusters. Compared with existing formulations, our new formulation (1) does not require explicitly modeling the user's dissatisfaction on job rescheduling, (2) enables the EMS to self-initiate jobs, (3) allows the user to initiate more flexible requests and (4) has a computational complexity linear in the number of devices. We also demonstrate the simulation results of applying Q-learning, one of the most popular and classical RL algorithms, to a representative example.