Estimating hourly cooling load in commercial buildings using a thermal network model and electricity submetering data

Abstract

One major obstacle in Heating, Ventilation and Air Conditioning (HVAC) system Fault Detection and Diagnostics (FDD), retrofitting and energy performance evaluation is the lack of detailed hourly cooling load data. Cooling load measurement in commercial buildings is expensive and sometimes very difficult to implement. Detailed building simulation models, such as EnergyPlus, are too complicated to build and also must be calibrated. In this paper, an hourly cooling load prediction model, called the “RC-S” model, is proposed. This new cooling load calculation approach consists of a simplified thermal network model of the building envelope, a thermal network model for the building internal mass and the internal cooling load model from the submetering system. One existing RC model is introduced as reference model and three types of “RC-S” models are set up in this study. Genetic algorithm (GA) is selected to optimize the parameters in those models. Measurement data collected from a real commercial building and simulation data obtained from EnergyPlus model of the same commercial building are used to train and test the four models. The results prove that the proposed “RC-S” cooling load calculation method is more accurate than the existing RC model and much simpler than whole building simulation models. It can provide reasonable estimations of cooling loads for HVAC FDD and other performance evaluations.