An improved office building cooling load prediction model based on multivariable linear regression

作者：Guo Qiang; Tian Zhe^*; Ding Yan; Zhu Neng

来源：Energy and Buildings, 2015, 107: 445-455.

DOI：10.1016/j.enbuild.2015.08.041

• Cooling load prediction
• Multivariable linear regression
• Principal component analysis
• Cumulative effect of high temperature
• Dynamic two-step correction

The cooling load prediction of heating, ventilating and air-conditioning (HVAC) systems in office buildings is fundamental work for optimizing the operation of HVAC systems. In this paper, an improved multivariable linear regression model is proposed to predict the daily mean cooling load of office buildings in which three main measures, including the principal component analysis (PCA) of meteorological factors, cumulative effect of high temperature (CEHT) and dynamic two-step correction, are used to improve prediction accuracy. The site measured cooling load of two office buildings in Tianjin is used to validate the model and evaluate the prediction accuracy. Meanwhile, four contrast models with one or two of the three measures are also built. A comparison among the models proves that a combination of the three measures could effectively improve the prediction accuracy. The predicted load of the proposed model has acceptable agreement with actual load, where the mean absolute relative error is less than 8%.

• 出版日期2015-11-15
• 单位天津大学