  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 during a demand response event. 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.
