1. A method performed using a computer, comprising: deriving, by a processor of the computer, a first weighted prediction using a trained naïve Bayesian predictor and using weather forecast data for a present day, wherein the first weighted prediction predicts future electrical generation by a photovoltaic system; deriving, by the processor of the computer, a second weighted prediction using a k-nearest neighbor predictor and using present day power generation data for the photovoltaic system, the present day power generation data acquired by way of instrumentation coupled to the computer and to the photovoltaic system, wherein the second weighted prediction predicts future electrical generation by the photovoltaic system; deriving, by the processor of the computer, a third weighted prediction using a motif predictor and using recent historical power generation data for the photovoltaic system, wherein the third weighted prediction predicts future electrical generation by the photovoltaic system; deriving, by the processor of the computer, a Bayesian model average of at least the first and the second and the third weighted predictions; predicting, by the processor of the computer, a near-future power generation by the photovoltaic system using the Bayesian model; and adjusting a number of loads on the photovoltaic system based on the prediction of the near-future power generation for the purpose of workload planning.