  The design of building heating, ventilation, and air conditioning (HVAC)
system is critically important, as it accounts for around half of building
energy consumption and directly affects occupant comfort, productivity, and
health. Traditional HVAC control methods are typically based on creating
explicit physical models for building thermal dynamics, which often require
significant effort to develop and are difficult to achieve sufficient accuracy
and efficiency for runtime building control and scalability for field
implementations. Recently, deep reinforcement learning (DRL) has emerged as a
promising data-driven method that provides good control performance without
analyzing physical models at runtime. However, a major challenge to DRL (and
many other data-driven learning methods) is the long training time it takes to
reach the desired performance. In this work, we present a novel transfer
learning based approach to overcome this challenge. Our approach can
effectively transfer a DRL-based HVAC controller trained for the source
building to a controller for the target building with minimal effort and
improved performance, by decomposing the design of neural network controller
into a transferable front-end network that captures building-agnostic behavior
and a back-end network that can be efficiently trained for each specific
building. We conducted experiments on a variety of transfer scenarios between
buildings with different sizes, numbers of thermal zones, materials and
layouts, air conditioner types, and ambient weather conditions. The
experimental results demonstrated the effectiveness of our approach in
significantly reducing the training time, energy cost, and temperature
violations.
