This study presents a new strategy using cluster analysis, multivariate adaptive regression splines and conditional inference trees to benchmark and evaluate building electricity usage. Different from the existing studies, cluster analysis was first used to group buildings based on their annual electricity usage patterns in order to improve the accuracy of the benchmarking result. Multivariate adaptive regression splines technique was then applied to capture the non-linear relationships (i.e. benchmarking models) between building electricity usage per square meter and explanatory factors with enhanced interpretability. Conditional inference trees technique was further used to evaluate the benchmarking result. The performance of this strategy was evaluated using two-year time-series electricity usage data of 20 university buildings. The results showed that the multivariate adaptive regression splines can effectively describe the complex non-linear relationships between building electricity usage per square meter and explanatory variables. The conditional inference trees can help identify conditions when buildings had higher electricity usage. The results obtained from this study can be further used to assist in building energy auditing, and fault detection and diagnosis.

这项研究提出了一种使用聚类分析，多元自适应回归样条和条件推理树的新策略，以基准测试和评估建筑物的用电量。与现有研究不同，聚类分析首先用于根据建筑物的年度用电量模式对建筑物进行分组，以提高基准结果的准确性。然后，应用多元自适应回归样条曲线技术来捕获每平方米建筑用电量与解释性因素之间的非线性关系（即基准模型），并增强解释性。条件推理树技术被进一步用于评估基准测试结果。使用20座大学建筑的两年时间序列用电量数据评估了该策略的性能。结果表明，多元自适应回归样条可以有效描述每平方米建筑用电与解释变量之间的复杂非线性关系。有条件的推理树可以帮助识别建筑物中用电量较高的情况。从这项研究中获得的结果可进一步用于协助建筑能耗审核以及故障检测和诊断。