SPAIOct 14, 2022

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

arXiv:2210.08103v263 citationsh-index: 31Has Code
Originality Synthesis-oriented
AI Analysis

This provides a resource for researchers and planners to study energy consumption impacts, addressing a data gap in the residential sector.

The authors tackled the lack of detailed residential energy-use data by releasing a large-scale synthetic dataset for the U.S., comprising hourly profiles for millions of households, validated through comparisons with reported data.

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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