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This paper discusses the development and testing of a single-speed direct expansion (DX) air coil model to predict compressor power, heat capacity, coefficient of performance (COP), and supply air temperature and humidity ratio as a function of airflow rate, air conditions entering the coil, and outdoor air temperature. The proposed model is calibrate dusing a nonlinear regression analysis of online data collected from a typical building automation system, if installed; short term data measurements; or manufacturer's data. The model parameters are determined through nonlinear regression instead of traditional methods, with parameters defined atrating conditions. To test the model, experiments were conducted on a fully instrumented, three-ton DX split-system air-conditioning unit. The unit ran over the course of several months to get a wide range of operating data. The testing results showed that the model can accurately capture the performance of DX air-conditioning system as compared to the existing EnergyPlus DX model. This proposed model can be a powerful tool used for several intelligent applications such as energy assessment, fault detection and diagnosis, and system performance optimization.

Citation: 2018 Winter Conference, Chicago, IL, Technical Papers