Scalable Supervisory HVAC Control for Linear Objectives

Abstract

Advanced control of heating, ventilation, and air-conditioning (HVAC) systems can substantially reduce energy costs and pollution. However, real-world adoption of popular algorithms among researchers, such as model predictive control and reinforcement learning, remains limited due in part to their high deployment and commissioning costs. Here, we develop two nearly commissioning-free supervisory controllers tailored to objectives that depend linearly on the controlled thermal load, such as energy costs and pollution. The controllers require, at most, two easily-estimable thermal parameters, forecasts of energy prices and occupant temperature preferences over a prediction horizon, and an indoor temperature measurement. In residential cooling simulations, both controllers perform essentially as well under traditional time-invariant electricity pricing as an omniscient optimal controller with exact model information and perfect forecasts, and attain up to 86.6% of the omniscient cost savings under increasingly prevalent time-varying pricing. These results suggest that simple, structure-exploiting controllers may capture most of the attainable value of advanced supervisory HVAC control with linear objectives, while avoiding the data, modeling, tuning, and computational burdens that hinder real-world deployment.