AbstractThe increased use of solar photovoltaic (PV) cells as energy sources on electric grids has created the need for more accessible solar irradiance and power production estimates for use in power modeling software. In the present paper, a novel technique for creating solar irradiance estimates is introduced. A solar PV resource dataset created by combining numerical weather prediction assimilation model variables, satellite data, and high-resolution ground-based measurements is also presented. The dataset contains ≈152 000 geographic locations each with ≈26 000 hourly time steps. The solar irradiance outputs are global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DIF). The technique is developed over the United States by training a linear multiple multivariate regression scheme at 10 locations. The technique is then applied to independent locations over the whole geographic domain. The irradiance estimates are input into a solar PV power modeling algorithm to compute solar PV power estimates for every 13-km grid cell. The dataset is analyzed to predict the capacity factors for solar resource sites around the United States for 2006–08. Statistics are shown to validate the skill of the scheme at geographic sites independent of the training set. In addition, it is shown that more high-quality, geographically dispersed, observation sites increase the skill of the scheme.
U.S. Geological Survey Geologic Carbon Dioxide Storage Resource Assessment of the United States
