Simulated Electricity: Synthetic Energy Consumption Data for ML

Simulated electricity consumption data created by Sebastian Fischer, associated with a 2023 research paper on conformal inference for group effects. The dataset is available under a CC-BY-4.0 license on OpenML, though its exact size and structure are not detailed in the provided metadata. Platform tags indicate it is a tabular, time-series simulation focused on electricity.

Use Cases

• Benchmarking time-series forecasting models based on simulated electricity consumption patterns.
• Testing conformal inference methods for prediction intervals on synthetic energy data.
• Analyzing simulated load profiles to understand potential grid scenarios.
• Training models for anomaly detection in energy consumption sequences.

Strengths

• Dataset is associated with a specific 2023 research paper, providing academic context.
• Released under the permissive CC-BY-4.0 license for open use.

Limitations

• Row count, column definitions, and sample data are unknown, limiting suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

OpenML, linked to a GitHub repository for a 2023 research paper.

Collection Method

Simulated/generated data, likely for methodological research.