Cooling load prediction and optimal operation of HVAC systems using a multiple nonlinear regression model

Abstract

Cooling load prediction is a critically important technology for ensuring energy conservation in cooling systems. Examples include advance air conditioning control, chiller startup, and controlling the number of chilled water pumps in operation. Many existing load prediction methods are normally arranged into three categories: energy simulation, artificial intelligence, and regression analysis. In contrast to the first two categories, regression analysis is practical and easy to implement in real applications. However, cooling load data always exhibits nonlinear, dynamic features, thus it is very difficult to accurately predict cooling loads with linear regression models. Thus, a multiple nonlinear regression (MNR) model for cooling systems in public buildings was used to accurately predict short term cooling loads in this study. The key variables in the MNR model are selected based on sensitivity analysis, and calibration methods can be used to enhance the prediction accuracy. The prediction process can be divided into three steps. The first step requires determining the weight coefficients on the input variables based on historical load and weather data. Second, the initially predicted load is obtained using the weight coefficients and predicted input variables. Finally, the initially predicted results were calibrated using relative calibration methods. The initial load prediction and final calibration are based on data from the same day, which is taken as a reference. The performance of the established MNR model is validated against a real case in Guangzhou using measured cooling load data and weather data. A case study shows that the accuracy of the final calibrated load is higher than the initially predicted load. The MNR model also produces more accurate results compared with three traditional regression models. The accurate cooling load prediction method can be used as an operation control strategy in real applications.