Hyperparameters of Baseline Models for Energy Forecasting
by Guoqiang Sun·Updated 2d ago
5.5 KB1files
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Description
Hyperparameters for ten baseline models evaluated in a study proposing a DG-LSTM-SA network for power generation and load demand forecasting. The dataset, authored by Guoqiang Sun and uploaded to figshare, is a 5.5 KB Excel file last updated on June 3, 2026. The models were tested on three real-world energy datasets: NEPOOL, Yichang, and Solar-Energy.
Use Cases
Benchmarking new forecasting models against established baselines based on the listed hyperparameters.
Reproducing the comparative study results from the associated research paper.
Analyzing the relationship between model configuration and performance in energy prediction tasks.
Training educational models on energy forecasting using documented baseline settings.
Strengths
Hyperparameters are documented for ten distinct baseline models, providing a comparative foundation.
The dataset is associated with a published research methodology evaluated on three real-world datasets.
Limitations
Column-level documentation is absent; field semantics must be inferred after download.
Row count is unknown, which may limit suitability assessment.
The dataset's small size (5.5 KB) suggests it contains only configuration parameters, not raw time-series data.
Provenance
Source
Guoqiang Sun
Collection Method
Likely extracted or defined as part of a machine learning research project.
Freshness
Last updated 2026-06-03 17:32:02; freshness should be verified.
Data is in XLS format; users may need compatible spreadsheet software.