Abstract
As the use of solar power as a source of electricity is increasing, so is the interest in modeling radiation at high temporal resolutions. High-dimensional remote sensing data products depend on cloud cover variability, atmosphere aerosol levels, and other atmospheric parameters. Because of weather fronts and aerosols, it is difficult to quantify solar power variability based on distributed solar networks. The global horizontal irradiance (GHI) component in the National Solar Radiation Database (NSRDB) is available at a 30-min time resolution. The algorithm proposed in this paper produces 1-min-resolution GHI samples at locations where NSRDB data are available. The synthetic irradiance datasets are produced using log-additive non-Gaussian mixture models and resampling techniques. The model is trained over historical data, and predicted values are compared with in situ data. This approach allows for estimating the solar irradiance at subhourly temporal resolutions, while featuring variability for locations where measurements are otherwise not available.
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