Virtual metering, greybox modelling, perimeter heaters, energy management, HVAC systems
Virtual metering provides a cost-effective alternative to physical meters to monitor building energy performance and capture unmetered energy flows at the zone-level. Virtual metering accuracy depends on the modelling method and its ability to represent the heating and cooling processes at a building thermal zone. This paper employs three virtual metering methods to estimate the heating energy of zone-level perimeter heaters: a steady-state modelling method, a transient modelling method, and a load disaggregation modelling method. Inverse models representing these three virtual metering methods are trained using data obtained from seven perimeter offices in an academic building in Ottawa, Canada. Model parameters are identified using the genetic algorithm and used for creating virtual meters that estimate the energy requirement of zone-level perimeter heaters. The virtual meters' accuracy is assessed by comparing the results to measured heating energy obtained from physical meters installed in the seven offices. The three virtual metering methods' performance is evaluated through illustrative examples in terms of modelling assumptions, data requirements, and virtual metering accuracy. The results indicate that the three virtual metering methods can estimate the daily heating energy supplied by perimeter heaters at a normalized root-mean-square error between 13% and 23%.