Evaluating multiple parameters dependency of base temperature for heating degree-days in building energy prediction  

作　　者：Qinglong Meng Yuan Xi Xingxing Zhang Monjur Mourshed Yue Hui 

机构地区：[1]School of Civil Engineering,Chang’an University,Xi’an,710061,China [2]School of Industrial Technology and Business Studies,Dalarna University,Falun,79188,Sweden [3]School of Engineering,Cardiff University,Cardiff,CF243AA,UK

出　　处：《Building Simulation》2021年第4期969-985,共17页建筑模拟（英文）

基　　金：The authors wish to thank the financial support received from various sources for conducting this research;This work was supported by the Key Research and Development Program of Shaanxi(No.2020NY-204);the Fundamental Research Funds for the Central Universities,CHD(No.300102289103).

摘　　要：To improve the prediction accuracy of heating demand, an appropriate base temperature should be estimated before using the heating degree-days (HDD) approach. This study collected the measured data for gas consumption at half-hourly resolution and the building physical characteristics from 89 educational buildings over four years. To determine the base temperature, in addition to the ambient temperature, more detailed independent variables, i.e. solar insolation, relative humidity, wind speed, and one-day ahead residual temperature, were incorporated into a three-parameter change-point multi-variable regression (3PH-MVR) for heating. The mean base temperature using the 3PH-MVR approach was about 0.4℃ lower than the results from the 3PH method only. The relationships between base temperature and annual HDD (based on 15.5℃), building location, and mean daily solar insolation were evaluated. It is found that the annual HDD and the daily insolation had clear impacts on base temperature, while there was a plausible relationship between base temperature and building location. Compared with traditional approach, the proposed 3PH-MVR method considers multiple weather parameters and determines a more robust base temperature, thus improving the prediction accuracy of HDD with higher average R2 value at 0.86 than that of univariate regression (0.82).

关 键 词：base temperature heating degree-days residual temperature change-point regression gas consumption 

分 类 号：TU83[建筑科学—供热、供燃气、通风及空调工程]