On-line Building Energy Optimization using Deep Reinforcement Learning

TL;DR

This paper demonstrates the application of deep reinforcement learning techniques, specifically Deep Q-learning and Deep Policy Gradient, for real-time building energy management using large-scale smart grid data.

Contribution

It introduces the first application of deep reinforcement learning for online building energy optimization in the smart grid context, extending methods to handle multiple actions.

Findings

Effective energy scheduling strategies validated on large-scale data.

Potential to provide real-time feedback for more efficient electricity use.

Deep RL methods outperform traditional approaches in this domain.

Abstract

Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure. These are expected to benefit planning and operation of the future power system, and to help the customers transition from a passive to an active role. In this paper, we explore for the first time in the smart grid context the benefits of using Deep Reinforcement Learning, a hybrid type of methods that combines Reinforcement Learning with Deep Learning, to perform on-line optimization of schedules for building energy management systems. The learning procedure was explored using two methods, Deep Q-learning and Deep Policy Gradient, both of them being extended to perform multiple actions simultaneously. The proposed approach was validated on the large-scale Pecan Street Inc. database. This highly-dimensional database includes information about photovoltaic power generation, electric vehicles as well as buildings appliances. Moreover, these on-line energy scheduling strategies could be used to provide real-time feedback to consumers to encourage more efficient use of electricity.