Day-Ahead Operational Forecast of Aggregated Solar Generation Assimilating Mesoscale Meteorology Information

System-Ievel (balancing area) forecasting errors can increase with growing shares of solar generation, thus increasing scheduling changes, area control errors, and system frequency variation. This investigation introduces a method to combine a spatial numerical weather forecast with solar generation time series to improve the performance of aggregated system-level day-ahead solar forecasts. The proposed method utilizes sparse learning to select spatial weather features (long-wave, short-wave, and clear-sky radiation) and Bayesian learning to predict the mean and variance in the forecast. The proposed method is probabilistic and preserves time structure in the predictive distribution. Different combinations of four sparse learning and three Bayesian learning methods are evaluated with four proper multivariate scoring rules to select the best model. Applying this method to the California Independent System Operator (CAISO) grid, the daily forecast improved by up to 10.4% relative to their forecast.