IEEE Power & Energy Magazine - May/June 2022 - 43
Real-World Experience of Machine
Learning in Demand Control
interval, the agent observes the state of the environment and
takes an action, making the environment transit from one
state to another. The environment can send feedback in the
form of a reward to the agent. The agent improves its action
based on that reward.
This process repeats until the environment reaches the
terminating state. The goal of the agent is to learn an action
policy that maximizes its cumulative reward from all states.
In this learning process, the agent does not rely on prior
knowledge to make decisions, but it gradually formulates an
optimal action policy through intelligent " trial-and-error. "
As a result, the model-free approach has greater flexibility
in solving control and optimization problems with
unknown models or partial observabilities.
Deep RL, which is a combination of deep learning and
RL, is a more recently developed approach. The key idea behind
deep RL is the adoption of a deep neural network
(DNN) to let the agent learn an optimal policy. As shown
in Figure 1, the DNN is a neural network with multiple hidden
layers. With the given input as the state, the DNN can
output either the estimated value of the actions or the optimal
action (depending on the specific deep RL approach) at the
current state due to the strong feature extraction ability of its
multiple hidden layers. Deep RL is model free since the DNN
is trained through interacting with and without prior knowledge
of the environment. In addition, the deep RL approach
also has high generalization abilities for new environments.
A well-trained DNN can be regarded as a function with finetuned
parameters. Whether a state as input is seen before or
not, a well-trained DNN can always generate an output.
In this article, we introduce an end-to-end workflow for
developing a deep RL-based residential HVAC controller
that can control multiple zones, where the zones represent
different floors in a house. In particular, we describe two
deep RL approaches for HVAC control, present the evaluation
of the deep RL-based HVAC control strategies through
simulation studies, and discuss the deployment of a deep
RL-based HVAC control approach in a real-world residential
house. Finally, we analyze the deployment results and
provide conclusions.
©SHUTTERSTOCK.COM/IGORDEYKA
may/june 2022
ieee power & energy magazine
43
http://www.SHUTTERSTOCK.COM/IGORDEYKA
IEEE Power & Energy Magazine - May/June 2022
Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - May/June 2022
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IEEE Power & Energy Magazine - May/June 2022 - Cover1
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