Efficient energy consumption is crucial for achieving sustainable energy goals in the era of climate change and grid modernization. Thus, it is vital to understand how energy is consumed at finer resolutions such as household in order to plan demand-response events or analyze the impacts of weather, electricity prices, electric vehicles, solar, and occupancy schedules on energy consumption. However, availability and access to detailed energy-use data, which would enable detailed studies, has been rare. In this paper, we release a unique, large-scale, synthetic, residential energy-use dataset for the residential sector across the contiguous United States covering millions of households. The data comprise of hourly energy use profiles for synthetic households, disaggregated into Thermostatically Controlled Loads (TCL) and appliance use. The underlying framework is constructed using a bottom-up approach. Diverse open-source surveys and first principles models are used for end-use modeling. Extensive validation of the synthetic dataset has been conducted through comparisons with reported energy-use data. We present a detailed, open, high-resolution, residential energy-use dataset for the United States.
翻译:在气候变化和电网现代化时代,高效能源消费对于实现可持续能源目标至关重要,因此,了解如何以家庭等更细的分辨率消耗能源至关重要,以便规划需求反应事件,或分析天气、电价、电动车辆、太阳能和占用时间对能源消耗的影响,然而,获得详细的能源使用数据以进行详细研究为目的的情况很少。在本文件中,我们为整个毗连的美国住宅部门发布了独特的大型合成住宅能源使用数据集,涵盖数百万个家庭。数据包括合成家庭的每小时能源使用概况,分列于热控制载荷(TCL)和应用程序使用中。基本框架是采用自下而上的方法构建的。在终端使用模型中使用了多样化的开放源调查和首个原则模型。通过对所报告的能源使用数据进行比较,对合成数据集进行了广泛的验证。我们为美国提供了详细的、开放的、高分辨率的住宅能源使用数据集。