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Due to the intermittency and uncertainty in photovoltaic (PV) power outputs, not only deterministic point predictions (DPPs), but also associated prediction Intervals (PIs) are important information for promoting the application of PV in practice, especially when grid connection continues to grow. While there are few studies focused on quantifying the uncertainty of forecasting PV power outputs, this paper developed a novel two-stage model to quantify the PIs of PV power outputs. In the first stage, three different neural networks, namely Generalized Regression Neural Network (GRNN), Extreme Learning Machine Neural Network (ELMNN) and Elman Neural Network (ElmanNN), were integrated using the Genetic Algorithms optimized Back Propagation (GA-BP) method to develop a Weight-Varying Combination Forecast Mode (WVCFM) model. The WVCFM model was applied to generate DPPs. In the second stage, a …