A method of forecasting for solar-based power systems, comprising the steps of: (a) measuring solar irradiance parameters with sensors for a defined geographical region over predetermined time intervals to form a data set; (b) selecting a window size defining a number of past measurements and future forecast predictions to be made from the number of past measurements; (c) partitioning the data set into successive and adjacent time series training data sequences of the selected window size; (d) applying a plurality of forecasting methods to the time series training data sequences to obtain future forecast predictions from each of the forecasting methods; (e) comparing the future forecast predictions of each of the forecasting methods to measured data to obtain a corresponding error rate associated with each of the methods, given the time series training data sequences; (f) assigning the forecasting method with the lowest error rate as the forecasting class for the time series training data sequences; (g) repeating steps (a) through (f) to train a classifier to determine an optimal forecasting class for different time series training data sequences; (h) using the sensors to measure current solar irradiance parameters; (i) using the classifier to determine the optimal forecasting class for the current solar irradiance parameters; (j) making future forecast predictions from the current solar irradiance parameters using the optimal forecasting class; (k) predicting solar-based power system demands and generating capacities based upon the future forecast predictions made in step (j); and (l) making adjustments in the solar-based power system demands and stored energy in order to maintain a substantially constant voltage supply for the geographic region. (a) measuring solar irradiance parameters with sensors for a defined geographical region over predetermined time intervals to form a data set; (b) selecting a window size defining a number of past measurements and future forecast predictions to be made from the number of past measurements; (c) partitioning the data set into successive and adjacent time series training data sequences of the selected window size; (d) applying a plurality of forecasting methods to the time series training data sequences to obtain future forecast predictions from each of the forecasting methods; (e) comparing the future forecast predictions of each of the forecasting methods to measured data to obtain a corresponding error rate associated with each of the methods, given the time series training data sequences; (f) assigning the forecasting method with the lowest error rate as the forecasting class for the time series training data sequences; (g) repeating steps (a) through (f) to train a classifier to determine an optimal forecasting class for different time series training data sequences; (h) using the sensors to measure current solar irradiance parameters; (i) using the classifier to determine the optimal forecasting class for the current solar irradiance parameters; (j) making future forecast predictions from the current solar irradiance parameters using the optimal forecasting class; (k) predicting solar-based power system demands and generating capacities based upon the future forecast predictions made in step (j); and (l) making adjustments in the solar-based power system demands and stored energy in order to maintain a substantially constant voltage supply for the geographic region. <EOS>