C28 -- Method for Identifying the Classification of Similar Building Types for Energy Benchmarking : EUI Perspective

Energy benchmarking of existing buildings is the process of identifying energy consumption levels and making retrofit decisions through the comparisons of similar buildings. Similar buildings are usually grouped by building type or region. However, the building types, which are classified for controlling permits and legal management purposes, may not suitable for energy benchmarking. Although a peer group identification based on energy consumption patterns, such as clustering, has been undertaken, new building types are required for energy benchmarking purposes by integrating detailed building types for a policy management perspective. In this study, appropriate classification of similar building types for benchmarking in terms of total energy use intensity (EUI) are proposed by merging building types in Korea. The six building types which were selected for energy management in South Korea have multiple sub-building types. In this study, after filtering the six building types and removing errors and outliers, 3,802 public buildings nationwide were selected for analysis. Statistical methods such as Kruskal-Wallis test, Welch's ANOVA were used for 2-year energy consumption data to analyse differences between groups and to verify differences in energy consumption by detailed use. The method to test the difference between groups was selected based on whether the normality and homogeneity of variance are satisfied. In addition, if a significant difference between groups was observed, the post hoc test was performed to establish the criteria for classifying building types for energy benchmarking. For the four building types which displayed a significant difference, a post-hoc test was performed for sub-building types to separate or combine the building types. Building types with similar operational characteristics displayed no difference in energy consumption. However, the suggested procedure was carried out based on the total EUI which requires an analysis of energy by end-uses such as heating and cooling. In addition, this study only considers the building types, so in future studies, a multivariate analysis of covariance will be required to assess the influence of several factor.