Abstract: The existing forecasting methods of medium and long term load curves are mostly point prediction methods,which can hardly make up for the inadequacy of the uncertainty analysis of power systems.For this reason,aprobabilistic forecasting and stochastic scenario simulation approach based on factor analysis and quantile regression neural network（QRNN）is proposed.By means of the factor analysis method,the dimensionality of daily load time series vector is reduced without sacrificing correlation of intraday load time series.A few independent load common factors are extracted and used as predictor variables.With the daily weather factors,weekday type and the corresponding common factor of the previous day as inputs,the QRNN models of load common factors considering the load correlation between adjacent days are developed.Based on these probabilistic forecasting models,the load curves of next 30days are predicted and simulated day by day,and stochastic scenarios of future month load curve are generated finally.The accuracy and high efficiency of the proposed approach have been verified by the results of an actual example.The correlation of load time series generated by the proposed approach is more accurate,being able to provide more accurate and all-round month-ahead load forecast information to system operators.