Individualized empirical baselines for evaluating the energy performance of existing buildings. (2nd January 2023)

• Record Type: Journal Article
• Authors: Lou, Yingli; Ye, Yunyang; Yang, Yizhi; Zuo, Wangda; Wang, Gang; Strong, Matthew; Upadhyaya, Satish; Payne, Chris
• Is Part Of: Science and technology for the built environment. Volume 29:Number 1(2023)
• Page Start: 19
• Page End: 33
• Publication Date: 2023-01-02
• Subjects: Heating -- Periodicals; Ventilation -- Periodicals; Air conditioning -- Periodicals; Refrigeration and refrigerating machinery -- Periodicals; Indoor air quality -- Periodicals; Indoor air quality; Air conditioning; Heating; Refrigeration and refrigerating machinery; Ventilation; Periodicals; 697
• DOI: 10.1080/23744731.2022.2134680
• Languages: English
• ISSNs: 2374-474X

Abstract

Abstract : The evaluation of building energy performance requires a baseline for comparison. Common empirical baselines are usually used for existing buildings since they are fast and convenient. However, the same type of building at the same location will receive the same baseline despite their difference in usage. Individualized baselines by creating building energy models are possible solutions, but it is labor intensive and time-consuming. To fill the gap, this study is to develop individualized empirical baselines for existing buildings in a fast way. First, common empirical baselines are created based on survey data. Then, to get training samples, building energy models for large-scale existing buildings are created and simulated. Finally, based on simulation results, mathematical models to get individualized empirical baselines in a fast way are created. U.S. medium office buildings were used as an example to demonstrate the method. We developed 30 mathematical models for medium office buildings in two vintages (constructed before 1980 and after 1980) and 15 climate zones. The mean absolute percentage errors (MAPE) between the individualized empirical baselines and the modeled baselines for those 30 mathematical models are all lower than 5.5%. An engineer can obtain the individualized empirical baseline for an existing building in a few seconds by using the open-source tool we developed.