Development of a cooling load prediction model for air-conditioning system control of office buildings

Abstract

Building cooling load prediction is of critical importance for achieving energy saving of air-conditioning system in office buildings. It not only benefits the energy-efficiency of the air-conditioning system, but is also important for the system stability. Many techniques have been developed for building cooling load prediction. These methods are normally arranged into three categories: regression analysis, energy simulation and artificial intelligence. Among them, the regression analysis methods are simple in mechanism and much practical for real application. However, traditional regression models are not sufficient to manage multi-parameter dynamic changes, and the outliers in prediction has not been well considered, making the accuracy of cooling load prediction not satisfactory. To promote the feasibility of regression methods for cooling load prediction of office buildings, an efficient regression model based on sensitivity analysis and the traditional autoregressive with exogenous (ARX) model (named as improved ARX model) is proposed in this paper. The improved ARX model keeps the constitution of ARX model, but uses specified variables that selected by sensitivity analysis. The quadratic terms of vital variables are included to reduce the impact of system non-linearity. A least square method is used to get the weight coefficient matrix for model training. Comparison studies are used to evaluate the prediction accuracy of the improved ARX model. The proposed model will largely improve prediction accuracy and more adaptive for real applications in the perspective of optimal control for HVAC systems.