Lessons learned from field demonstrations of model predictive control and reinforcement learning for residential and commercial HVAC: A review

Abstract

A large body of simulation research suggests that model predictive control (MPC) and reinforcement learning (RL) for heating, ventilation, and air-conditioning (HVAC) in residential and commercial buildings could reduce energy costs, pollutant emissions, and strain on power grids. Despite this potential, neither MPC nor RL has seen widespread industry adoption. Field demonstrations could accelerate MPC and RL adoption by providing real-world data that support the business case for deployment. Here we review 24 papers that document field demonstrations of MPC and RL in residential buildings and 80 in commercial buildings. After presenting demographic information – such as experiment scopes, locations, and durations – this paper analyzes experiment protocols and their influence on performance estimates. We find that 71 % of the reviewed field demonstrations use experiment protocols that may lead to unreliable performance estimates. Over the remaining 29 % that we view as reliable, the weighted-average cost savings, weighted by experiment duration, are 16 % in residential buildings and 13 % in commercial buildings. While these savings are potentially attractive, making the business case for MPC and RL also requires characterizing the costs of deployment, operation, and maintenance. Only 13 of the 104 reviewed papers report these costs or discuss related challenges. Based on these observations, we recommend directions for future field research, including: Improving experiment protocols; reporting deployment, operation, and maintenance costs; designing algorithms and instrumentation to reduce these costs; controlling HVAC equipment alongside other distributed energy resources; and pursuing emerging objectives such as peak shaving, arbitraging wholesale energy prices, and providing power grid reliability services.