Quantitative Methods for Comparing Different HVAC Control Schemes

TL;DR

This paper introduces quantitative metrics and statistical methods for rigorously comparing HVAC control schemes' energy efficiency and occupant comfort amidst environmental variability, demonstrated through case studies.

Contribution

It develops a novel methodology for quantitatively and statistically comparing HVAC controllers, accounting for environmental fluctuations, with practical case studies.

Findings

Schedule controller vs. default controller energy comparison

Hybrid learning-based MPC controller performance analysis

Methodology applicable to other building automation systems

Abstract

Experimentally comparing the energy usage and comfort characteristics of different controllers in heating, ventilation, and air-conditioning (HVAC) systems is difficult because variations in weather and occupancy conditions preclude the possibility of establishing equivalent experimental conditions across the order of hours, days, and weeks. This paper is concerned with defining quantitative metrics of energy usage and occupant comfort, which can be computed and compared in a rigorous manner that is capable of determining whether differences between controllers are statistically significant in the presence of such environmental fluctuations. Experimental case studies are presented that compare two alternative controllers (a schedule controller and a hybrid system learning-based model predictive controller) to the default controller in a building-wide HVAC system. Lastly, we discuss how our proposed methodology may also be able to quantify the efficiency of other building automation systems.