NEPOOL, Yichang, and Solar-Energy Power Generation and Load Demand Statistics

Three real-world energy datasets were used to evaluate a novel forecasting model called DG-LSTM-SA. Guoqiang Sun published these data statistics on figshare in June 2026. The datasets likely contain time-series records of power generation and load demand.

Use Cases

• Benchmarking new forecasting algorithms based on real-world power generation data.
• Training models to predict load demand for grid operational decision-making.
• Comparing computational efficiency of models using standardized energy time-series.
• Analyzing temporal patterns in power generation across different energy sources.
• Evaluating model accuracy reductions in Mean Absolute Error for energy systems.

Strengths

• Data is associated with a published model evaluation showing a >75% reduction in MAE compared to standard RNNs.
• The datasets represent three distinct real-world energy systems (NEPOOL, Yichang, Solar-Energy).
• The file is licensed under CC-BY-4.0, allowing open use and sharing.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The 5.5 KB file size suggests a very limited scope, likely containing summary statistics rather than raw operational data.

Provenance

Source

figshare

Collection Method

Likely extracted or aggregated from real-world energy system operations.

Freshness

Last updated 2026-06-03 17:32:03; freshness should be verified.

Geography

Likely includes data from NEPOOL (North American region) and Yichang (China), plus solar energy sources.