Title: On-line Building Energy Optimization using Deep Reinforcement Learning,
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.