Datasets & Results for Probabilistic Day-Ahead Forecasting of System-Level Renewable Energy and Electricity Demand

This data repository contains the datasets and results for replicating the experiments in the journal article Probabilistic Day-Ahead Forecasting of System-Level Renewable Energy and Electricity Demand and the processing publication Day-Ahead Operational Forecast of Aggregated Solar Generation Assimilating Mesoscale Meteorology Information. The codes for the models and the experiments carried out in this research are on a public GitHub repository https://github.com/gterren/caiso_power The codes (Jupyter Notebooks) for the visualization companion of this research are available on a public GitHub repository https://github.com/gterren/caiso_power_viz This data repository contains multiple .zip files. Their descriptions are: models.zip models (.pkl) and the validation results (.csv) used on the visualizations. extra.zip data extracted from CAISO and WECC monthly reports (.csv) and power stations in California (.csv). population.zip spatial population density maps (.asc). maps.zip CAISO, California, and USA maps files (.shp). auxiliary.zip solar, wind, and population masks (.pkl). dataset-2023.zip postprocess dataset (.dat) from raw data. actuals.zipactual solar generation, wind generation, and electricity demand (.pkl). processed.zip data files (.pkl) after preprocessing (actual datasets used in the experiments).