An evidence theory-based uncertainty quantification method of building thermal parameters for accurate reliability assessment of building air-conditioning design loads

Probabilistic methods are frequently employed in the reliability assessment of building air-conditioning design loads to quantify the uncertainty associated with building thermal parameters. However, its implementation requires some assumptions because of inadequate data in the early stages of design. These assumptions cause the uncertainty quantification results of the building thermal parameters to differ from reality, which may lead to a significant overestimation or underestimate of reliability. Therefore, an evidence theory-based method was proposed to quantify the uncertainty of the building thermal parameters. In this method, evidence theory was employed instead of probability theory because the former could make full use of small sample information to achieve objective uncertainty quantification. Taking an office building in Tianjin, China, as an example, the new method was tested, and its effectiveness was verified. The results showed that the uncertainty quantification results of the building thermal parameters based on the new method included those of the benchmark method. Under small sample conditions, the reliability assessment results based on the new method were closer to those of the benchmark compared with the traditional method.