The 2006 Commercial End Use Survey (CEUS) database developed by the California Energy Commission is a far richer source of energy end-use data for non-residential buildings than has previously been available and opens the possibility of creating new and more powerful energy benchmarking processes and tools. In this article-Part 2 of a two-part series-we describe the methodology and selected results from an action-oriented benchmarking approach using the new CEUS database. This approach goes beyond whole-building energy benchmarking to more advanced end-use and component-level benchmarking that enables users to identify and prioritize specific energy efficiency opportunities-an improvement on benchmarking tools typically in use today.

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This paper presents the curation of a monitored dataset from an office building constructed in 2015 in Berkeley, California. The dataset includes whole-building and end-use energy consumption, HVAC system operating conditions, indoor and outdoor environmental parameters, as well as occupant counts. The data were collected during a period of three years from more than 300 sensors and meters on two office floors (each 2,325 m2) of the building. A three-step data curation strategy is applied to transform the raw data into research-grade data: (1) cleaning the raw data to detect and adjust the outlier values and fill the data gaps; (2) creating the metadata model of the building systems and data points using the Brick schema; and (3) representing the metadata of the dataset using a semantic JSON schema. This dataset can be used in various applications—building energy benchmarking, load shape analysis, energy prediction, occupancy prediction and analytics, and HVAC controls—to improve the understanding and efficiency of building operations for reducing energy use, energy costs, and carbon emissions.

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