  Advanced building control methods such as model predictive control (MPC)
offer significant potential benefits to both consumers and grid operators, but
the high computational requirements have acted as barriers to more widespread
adoption. Local control computation requires installation of expensive
computational hardware, while cloud computing introduces data security and
privacy concerns. In this paper, we drastically reduce the local computational
requirements of advanced building control through a reinforcement learning
(RL)-based approach called Behavioral Cloning, which represents the MPC policy
as a neural network that can be locally implemented and quickly computed on a
low-cost programmable logic controller. While previous RL and approximate MPC
methods must be specifically trained for each building, our key improvement is
that our controller can generalize to many buildings, electricity rates, and
thermostat setpoint schedules without additional, effort-intensive retraining.
To provide this versatility, we have adapted the traditional Behavioral Cloning
approach through (1) a constraint-informed parameter grouping (CIPG) method
that provides a more efficient representation of the training data; (2) an
MPC-Guided training data generation method using the DAgger algorithm that
improves stability and constraint satisfaction; and (3) a new deep learning
model-structure called reverse-time recurrent neural networks (RT-RNN) that
allows future information to flow backward in time to more effectively
interpret the temporal information in disturbance predictions. The result is an
easy-to-deploy, generalized behavioral clone of MPC that can be implemented on
a programmable logic controller and requires little building-specific
controller tuning, reducing the effort and costs associated with implementing
smart residential heat pump control.
