Simulated Electricity Demand Data for Forecasting

Simulated_electricity is a dataset for energy forecasting, likely containing time-series data on electricity demand. The dataset was created by Sebastian Fischer and is associated with a 2023 research paper on conformal inference for graph neural networks. It is shared under a CC-BY-4.0 license on the OpenML platform.

Use Cases

• Forecasting electricity demand based on simulated time-series patterns.
• Benchmarking graph neural network models for energy prediction tasks.
• Testing conformal inference methods for uncertainty quantification in energy forecasting.
• Analyzing simulated load profiles for grid planning and management.

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

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and dataset size are unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Sebastian Fischer

Collection Method

Simulated data, likely generated for research on conformal inference for graph neural networks.