Solar output power forecast using an ensemble framework with neural predictors and Bayesian adaptive combination

An Accurate forecast of PV output power is essential to optimize the relationship between energy supply and demand. However, it is a challenging task due to the intermittent nature of solar PV output and the effect of number of meteorological variables on it. In this paper, a multivariate neural network (NN) ensemble forecast framework is proposed. First multiple neural predictors are trained with input data from meteorological variables and then accurate predictors are combined with a Bayesian model averaging (BMA) technique. To identify the best performing framework, three different NN ensemble networks are created, namely feedforward neural network (FNN), Elman backpropagation network (ELM) and cascade-forward backpropagation (NewCF) network and trained with three different training techniques. The real time recorded solar PV data along with meteorological variables of the University of Queensland's solar facility from 2014 to 2015 is used. To validate the forecast framework, one day ahead (24 h) forecasts are selected for different seasons. The results show that the proposed ensemble framework substantially improves the forecast accuracy of PV power output as compared benchmark methods, particularly for short term forecasting horizons.