Investigating critical model input features for unitary air conditioning equipment

A variable-speed air-conditioning system provides more control than single-or multi-stage units, resulting in increased energy efficiency and more precise temperature control. Current commercial and residential direct expansion (DX) equipment testing standards require the embedded control placed on variable-speed equipment to be deactivated during performance testing and instead the fan and compressor speeds are fixed. For this reason, the test and field performances differ, creating a performance as well as an energy consumption gap. This paper presents a machine learning-based approach to investigate critical features from the air and refrigerant side influencing the cooling capacity of unitary air conditioning equipment. High-fidelity experimental data obtained from 4 different state-of-the-art high-efficiency units, 1 fixed-speed 35.2(10) kW(tons) commercial and 3 variable-speed residential unitary equipment of 12.3(3.5), 14(4), and 17.6(5) kW(tons) of cooling capacity have been analyzed by utilizing a feature selection methodology, Elastic Net (EN). Influential features with higher relevance scores corresponding to the total cooling capacity are fed into a supervised Artificial Neural Network (ANN), formulated as a predictive model to evaluate the validity of features selected. Results show that the proposed method of feature selection when applied, ANN is able to predict the equipment cooling capacity with a mean absolute percent error of less than 2% for all tested units, thus approving the proposed input features. The findings may be used as recommendations for the development of a semi-empirical model for better cooling capacity and COP predictions.