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Photovoltaic (PV) output is influenced by many meteorological factors. The significant degree of meteorological data influences the accuracy of forecasting result. This paper proposed a short-term PV output forecasting method while the weather data and PV output data were weak correlated. By analyzing meteorological historical data and PV output historical data, the main factors effecting PV generation were found out by Pearson correlation coefficient. Based on relevant factors, fuzzy clustering analysis method was used to select similar days, and then support vector regression (SVR) forecasting model was built. SVR model has excellent learning ability for small sample. To determine model parameters, a two-step method was proposed. First, using the global search method to determine the value of parameter ε and the appropriate range of kernel parameter p and regularization parameter C, then using self-adaptive differential evolution algorithm to find the optimal p and C, in order to improve the forecast accuracy when parameter ε was selected in large scale. Examples show that the method proposed in this paper has good forecasting ability when the weather data and PV output data are weak correlated. Keywords-distributed photovoltaic; PV output forecasting; support vector machine; parameter selected

Short-Term Photovoltaic Output Forecasting with Weakly Related Meteorological Data