مدل بهبودیافته پیش بینی بار سرمایشی ساختمان اداری بر مبنای رگرسیون خطی چند گانه
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|46639||2015||11 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Energy and Buildings, Volume 107, 15 November 2015, Pages 445–455
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%.