High-resolution synthetic residential energy use profiles for the United States
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Abstract (arXiv)
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.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 1 of its 100 references that are in the map. First posted on arXiv 14 Oct 2022.
Builds on
1 of 100 references.
References · 1
- 2014Metadata for Energy Disaggregationcited by 1
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Citing papers per year: 2024: 3; 2025: 3; 2026: 4.
10 papers.
- 2025CENTS: Generating synthetic electricity consumption time series for rare and unseen scenarioscited by 3
- 2025A synthetic dataset of French electric load curves with temperature conditioningcited by 2
- 2026Are LLMs Ready for Neural-integrated Mechanistic Modeling? A Benchmark and Agentic Frameworkcited by 1
- 2024A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Thresholdcited by 0
- 2024A Data Mining-Based Dynamical Anomaly Detection Method for Integrating with an Advance Metering Systemcited by 0
- 2024Differentially Private Publication of Electricity Time Series Data in Smart Gridscited by 0
- 2025Learning and Generating Diverse Residential Load Patterns Using GAN with Weakly-Supervised Training and Weight Selectioncited by 0
- 2026RESCAST-100K: A Comprehensive Dataset for Cross-Domain Residential Load and Indoor Temperature Forecastingcited by 0
- 2026SynEnergy: Anomaly Semantic-Guided Diffusion for Synthetic Energy Data Generationcited by 0
- 2026LoaDiff: Conditional Generation of Electricity Consumption Time Series for Energy Analyticscited by 0
- 2026LoaDiff: Conditional Generation of Electricity Consumption Time Series for Energy Analytics10 Sep 2026
- 2026SynEnergy: Anomaly Semantic-Guided Diffusion for Synthetic Energy Data Generation4 Aug 2026
- 2026RESCAST-100K: A Comprehensive Dataset for Cross-Domain Residential Load and Indoor Temperature Forecasting1 Jun 2026
- 2026Are LLMs Ready for Neural-integrated Mechanistic Modeling? A Benchmark and Agentic Framework20 Feb 2026
- 2025Learning and Generating Diverse Residential Load Patterns Using GAN with Weakly-Supervised Training and Weight Selection19 Apr 2025
- 2025A synthetic dataset of French electric load curves with temperature conditioning18 Apr 2025
- 2025CENTS: Generating synthetic electricity consumption time series for rare and unseen scenarios24 Jan 2025
- 2024Differentially Private Publication of Electricity Time Series Data in Smart Grids24 Aug 2024
- 2024A Data Mining-Based Dynamical Anomaly Detection Method for Integrating with an Advance Metering System4 May 2024
- 2024A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Threshold5 Apr 2024