ArticleScientific data2023
High resolution synthetic residential energy use profiles for the United States.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 47 citations in OpenAlex.
- Tunable and computationally efficient framework for retrofitting: Budget allocation using income and spatial equity.PNAS nexus · 2026Article
- CHARGE-MAP: An integrated framework to study the multicriteria EV charging infrastructure expansion problem.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A dataset for understanding self-reported patterns influencing residential energy decisions.Scientific data · 2025Article
- A dataset of income distribution on provincial, urban, and rural levels for China from 2020 to 2100.Scientific data · 2024Article
- A multi-year campus-level smart meter database.Scientific data · 2024Article
- HYDROSAFE: A Hybrid Deterministic-Probabilistic Model for Synthetic Appliance Profiles Generation.Sensors (Basel, Switzerland) · 2024Article
- Extend the ProFamy cohort-component method to conduct probabilistic households and living arrangement projections.China population and development studies · 2024Article
- High-resolution electric power load data of an industrial park with multiple types of buildings in China.Scientific data · 2023Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 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, digital-twin of 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.