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Advanced energy algorithms running at big-data scale will be necessary to identify, realize, andverify energy savings to meet government and utility goals of building energy efficiency. Anyalgorithm must be well characterized and validated before it is trusted to run at these scales.Smart meter data from real buildings will ultimately be required for the development, testing, andvalidation of these energy algorithms and processes. However, for initial development and testing,smart meter data are difficult to work with due to privacy restrictions, noise from unknown sources,data accessibility, and other concerns which can complicate algorithm development and validation.This study describes a new methodology to generate synthetic smart meter data of electricityuse in buildings using detailed building energy modeling, which aims to capture the variabilityand stochastics of real energy use in buildings. The methodology can create datasets tailored torepresent specific scenarios with known truth and controllable amounts of synthetic noise.Knowledge of ground truth also allows the development and validation of enhanced processeswhich leverage building metadata, such as building type or size (floor area), in addition to smartmeter data. The methodology described in this paper includes the key influencing factors ofreal-world building energy use including weather data, occupant-driven loads, building operationand maintenance practices, and special events. Data formats to support workflows leveraging bothsynthetic meter data and associated metadata are proposed and discussed. Finally, example usecases of the synthetic meter data are described to illustrate potential applications.