Effect of Thermal Energy Meter Density and Configuration on Disaggregation Accuracy

Submetering significantly enhances building performance monitoring capabilities by providing a higher resolution to resource use and revealing energy consumption across the building and systems that would otherwise remain hidden. Previous studies indicated that building automation system (BAS) trend data represents an untapped opportunity to disaggregate existing submeter data for heating, cooling, and electricity into zone- and system-level end-uses. Algorithms can disaggregate bulk meter data by looking at trend data that provide contextual information regarding the operating status of energy-consuming equipment. However, the level of submetering required to enable end-use disaggregation has yet to be studied. To this end, this paper investigates the effect of submeter density and configuration on the performance of a regression-based disaggregation strategy using BAS trend data as predictors. The method was evaluated using synthetic meter and heating and reheat coil valve and perimeter heating device status trend data generated through a building performance simulation (BPS) model of a government office building in Ottawa, Canada. The results indicate the minimum number of heating energy submeters needed to be installed in a building for accurate zone- and system-level disaggregation. The methodology presented in our paper can also inform changes in building design codes and standards regarding the minimum density and appropriate configuration of the metering.