From 2009 to 2022, this dataset provides machine learning estimates for residential electricity consumption across the United States. The estimates are spatiotemporally explicit, meaning they are detailed across both space and time. The data was created by author Yu Ying and is hosted on the ODUM Harvested Dataverse.
Use Cases
- Modeling residential energy demand trends based on spatiotemporal estimates
- Analyzing regional electricity consumption patterns based on the U.S. coverage
- Benchmarking machine learning predictions for energy consumption based on the described methodology
- Assessing the impact of policies or events on household electricity use based on the 14-year time range
Strengths
- Estimates span 14 years from 2009 to 2022
- Coverage includes the entire United States
- Data is spatiotemporally explicit, providing detailed spatial and temporal granularity
Limitations
- Column-level documentation is absent; field semantics must be inferred after download
- Row count is unknown, which may limit suitability assessment
Provenance
- Source
- ODUM Harvested Dataverse
- Collection Method
- Machine learning estimates
- Time Range
- 2009–2022
- Freshness
- Last updated 2026-05-18 03:10:20; freshness should be verified
- Geography
- United States