1. A method of evaluating solar energy forecasts, the method comprising: generating power at a virtual power plant, the virtual power plant including at least one solar power generating device; obtaining a test dataset of historical irradiance at a location of the solar power generating device; normalizing the test dataset based on a clear sky model of irradiance at the location by dividing a point in the test dataset at a given time by a clear sky irradiance at the given time; plotting a histogram of the test dataset, the histogram relating a probability density of clear sky indexes; fitting a histogram curve to the histogram, the histogram curve including a combination of class-specific curves for each of a plurality of classes; extracting the class-specific curves for each of the classes; clustering each point in the test dataset with one of the classes based on how closely each point fits to the class-specific curves; obtaining a forecast of irradiance for a time at the solar power generating system; finding a nearest neighbor of the forecast in the test dataset based on similarities between the forecast of irradiance and the test dataset, the nearest neighbor including a point in the test dataset with similar weather conditions to the forecast; based on the nearest neighbor of the forecast, classifying the forecast into one of the plurality of classes to which the nearest neighbor belongs; determining confidence intervals of the forecast based on a degree of unpredictability of irradiance of the one of the plurality of classes; and based on the confidence intervals, modifying power production at the virtual power plant by performing one of increasing output or decreasing output of the solar power generating device based on the confidence intervals.