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Current building energy benchmarking systems categorize buildings into peer groups by static characteristics such as climate zones and building types, which cannot account for the huge variation in building operations. Grouping buildings with diverse operations for benchmarking could result in misleading results. The smart meters provide an opportunity to feature the dynamic characteristics of building operations, but proper data mining techniques are needed to use the data for benchmarking. Accordingly, this paper proposes a framework that makes use of the time-series energy consumption data to categorize buildings by their operations and conduct energy benchmarking within each category. The proposed framework is based on 3-step K-means clustering and consists of two main parts: (1) Operation quantification, and (2) Building categorization and benchmarking. The framework was tested on a dataset of …