An improved ARX model for hourly cooling load prediction of office buildings in different climates

Abstract

Abstract. An attempt was made to develop an improved autoregressive with exogenous (ARX) model for office buildings cooling load prediction in five major climates of China. The cooling load prediction methods can be arranged into three categories: regression analysis, energy simulation, and artificial intelligence. Among them, the regression analysis methods using regression models are much simple and practical for real applications. However, traditional regression models are often helpless to manage multiparameter dynamic changes, making it not accurate as the other two categories. Many of the existing cooling load prediction studies use piecewise linearization to manage nonlinearity. To improve the prediction accuracy of regression analysis methods, higher order and interaction terms are included in improved ARX based on traditional ARX model. The improved ARX model consists of eight variables, with eleven coefficients accessed at a time. For applications and evaluations, an office building in major cities within each climatic zone was selected as a representation. These cities were Harbin, Beijing, Nanjing, Kunming and Guangzhou respectively. The coefficient of determination R 2 is greater than 0.9 in five cities. The prediction results show that the improved ARX model can adapt to different climatic conditions, including those nonlinearity cases.

Cooling Load Forecasting via Predictive Optimization of a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model

“…As the literature shows, predictions that use machine learning algorithms offer advantages in that they do not require complex modeling compared to simulation methods that are based on mathematical models. As shown, researchers have conducted various studies of heating and cooling load prediction methods that employ machine learning algorithms [12][13][14][15][16]. However, in order to improve the accuracy of such predictions, the researchers either combined several neural network models or utilized complex structures, such as deep layers.…”