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

arXiv: https://arxiv.org/abs/2210.08103 · HTML: https://mlsanity.com/paper/2210.08103/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 100 references that are in the map.
- First posted on arXiv 14 Oct 2022.

## Builds on (most cited first, 1 of 1)

- [Metadata for Energy Disaggregation](https://mlsanity.com/paper/1403.5946/) (2014)

## Built on by (most cited first, 10 of 10)

- [CENTS: Generating synthetic electricity consumption time series for rare and unseen scenarios](https://mlsanity.com/paper/2501.14426/) (2025)
- [A synthetic dataset of French electric load curves with temperature conditioning](https://mlsanity.com/paper/2504.14046/) (2025)
- [Are LLMs Ready for Neural-integrated Mechanistic Modeling? A Benchmark and Agentic Framework](https://mlsanity.com/paper/2602.18008/) (2026)
- [SynEnergy: Anomaly Semantic-Guided Diffusion for Synthetic Energy Data Generation](https://mlsanity.com/paper/2608.03087/) (2026)
- [A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Threshold](https://mlsanity.com/paper/2404.04311/) (2024)
- [A Data Mining-Based Dynamical Anomaly Detection Method for Integrating with an Advance Metering System](https://mlsanity.com/paper/2405.02574/) (2024)
- [Differentially Private Publication of Electricity Time Series Data in Smart Grids](https://mlsanity.com/paper/2408.16017/) (2024)
- [Learning and Generating Diverse Residential Load Patterns Using GAN with Weakly-Supervised Training and Weight Selection](https://mlsanity.com/paper/2504.14300/) (2025)
- [RESCAST-100K: A Comprehensive Dataset for Cross-Domain Residential Load and Indoor Temperature Forecasting](https://mlsanity.com/paper/2606.02852/) (2026)
- [LoaDiff: Conditional Generation of Electricity Consumption Time Series for Energy Analytics](https://mlsanity.com/paper/2609.11639/) (2026)

## 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.