Humidity-Aware Model Predictive Control for Residential Air Conditioning: A Field Study

TL;DR

This study develops a machine-learning model for indoor humidity dynamics and evaluates its impact on predictive control of residential air conditioning, showing that accurate humidity modeling improves demand response but has less effect on energy cost savings.

Contribution

It introduces a scalable machine-learning approach for modeling indoor humidity dynamics and assesses its significance in predictive control through real-world field tests.

Findings

Humidity-aware models reduce demand response violations.

Energy cost savings are similar regardless of humidity model accuracy.

Accurate humidity modeling is crucial for nonlinear control objectives.

Abstract

Model predictive control of residential air conditioning could reduce energy costs and greenhouse gas emissions while maintaining or improving occupants' thermal comfort. However, most approaches to predictive air conditioning control either do not model indoor humidity or treat it as constant. This simplification stems from challenges with modeling indoor humidity dynamics, particularly the high-order, nonlinear equations that govern heat and mass transfer between the air conditioner's evaporator coil and the indoor air. This paper develops a machine-learning approach to modeling indoor humidity dynamics that is suitable for real-world deployment at scale. This study then investigates the value of humidity modeling in four field tests of predictive control in an occupied house. The four field tests evaluate two different building models: One with constant humidity and one with time-varying humidity. Each modeling approach is tested in two different predictive controllers: One that focuses on reducing energy costs and one that focuses on constraining electric power below a utility-specified threshold. The two models lead to similar performance for reducing energy costs. Combining the results of this study and a prior heating study of the same house, the estimated year-round energy cost savings were $340-497 or 22-31% (95% confidence intervals); these savings were consistent across both humidity models. However, in the demand response tests, the simplifying assumption of constant humidity led to far more frequent and severe violations of the power constraint. These results suggest that accurate building models are important for nonlinear objectives, such as reducing or constraining peak demand, while for linear objectives such as reducing energy costs or emissions, model accuracy is less important.