Virtual metering of heat supplied by zone-level perimeter heaters: An investigation with three inverse modelling approaches

Abstract

Virtual metering at the equipment level provides a non-intrusive tool to understand energy flows in commercial buildings and monitor building energy performance. The accuracy of virtual meters depends on the modelling approach and its ability to capture the underlying physical processes. This paper presents a novel comparison and provides guidance for the selection of inverse modelling approaches used to create virtual meters to estimate the heat supplied by zone-level hydronic perimeter heaters. The approaches investigated are: steady-state inverse greybox modelling, transient inverse greybox modelling, and water-side load disaggregation. Models that represent these three approaches are trained using data from a highly instrumented academic building in Ottawa, Canada. Model parameters are identified using the genetic algorithm and used in creating virtual meters that can estimate the heat added by perimeter heaters into building variable air volume zones. The accuracy of these virtual meters is assessed by comparing them to physical meters installed in these zones. The results indicate that the three modelling approaches can estimate the daily heating energy supplied by perimeter heaters at a normalized root-mean-square error between 16% and 18%. However, the accuracy of the virtual meters declines as the number of zones increases. The performance of the three modelling approaches is evaluated through illustrative examples that compare modelling assumptions, data requirements, data processing, and model prediction accuracy.