Advanced energy algorithms running at big-data scale will be necessary to identify, realize, and verify energy savings to meet government and utility goals of building energy efficiency. Any algorithm 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, and validation 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 electricity use in buildings using detailed building energy modeling, which aims to capture the variability and stochastics of real energy use in buildings. The methodology can create datasets tailored to represent specific scenarios with known truth and controllable amounts of synthetic noise. Knowledge of ground truth also allows the development and validation of enhanced processes which leverage building metadata, such as building type or size (floor area), in addition to smart meter data. The methodology described in this paper includes the key influencing factors of real-world building energy use including weather data, occupant-driven loads, building operation and maintenance practices, and special events. Data formats to support workflows leveraging both synthetic meter data and associated metadata are proposed and discussed. Finally, example use cases of the synthetic meter data are described to illustrate potential applications.

在大数据规模上运行的先进能源算法对于识别，实现和验证能源节约，以实现政府和公共事业的建筑节能目标将是必要的。任何算法都必须经过充分表征和验证，然后才能在这些规模上运行。这些能源算法和过程的开发，测试和验证最终将需要来自真实建筑物的智能电表数据。但是，对于初始开发和测试，由于隐私限制，未知来源的噪声，数据可访问性以及其他可能使算法开发和验证变得复杂的问题，智能电表数据难以使用。这项研究描述了一种新的方法，该方法可使用详细的建筑能耗模型来生成建筑物的用电智能电表数据，其目的是捕获建筑物中实际能源使用的可变性和随机性。该方法可以创建量身定制的数据集，以表示具有已知事实和可控制数量的合成噪声的特定场景。了解地面真相还可以开发和验证增强的过程，这些过程除了利用智能电表数据外，还利用建筑物元数据（例如建筑物类型或大小（地板面积））。本文介绍的方法包括现实世界建筑能耗的关键影响因素，包括天气数据，乘员驱动的负荷，建筑运营和维护实践以及特殊事件。提出并讨论了支持综合仪表数据和相关元数据的工作流的数据格式。最后，