论文标题

部分可观测时空混沌系统的无模型预测

High-resolution synthetic residential energy use profiles for the United States

论文作者

Thorve, Swapna, Baek, Young Yun, Swarup, Samarth, Mortveit, Henning, Marathe, Achla, Vullikanti, Anil, Marathe, Madhav

论文摘要

有效的能源消耗对于在气候变化和电网现代化时代实现可持续能源目标至关重要。因此,重要的是要了解如何在家庭(例如家庭)中消耗能源,以计划需求响应事件或分析天气,电价,电动汽车,太阳能和占用时间表对能源消耗的影响。但是,很少有可用性和访问详细的能源使用数据,这将实现详细的研究。在本文中,我们发布了一个独特的,大规模的,合成的,住宅的能源使用数据集,适用于整个连续美国的住宅部门,覆盖了数百万个家庭。该数据包括用于合成家庭的小时能量使用曲线,分解为恒温控制载荷(TCL)和设备使用。基础框架是使用自下而上的方法构建的。多种开源调查和第一原理模型用于最终使用模型。通过与报告的能量使用数据进行比较,对合成数据集进行了广泛的验证。我们为美国提供了一个详细,开放,高分辨率,住宅能源使用数据集。

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.

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