Abstract: In order to reduce the influence of the uncertainty of photovoltaic power generation on the power system and describe the photovoltaic power output interval more accurately and clearly,a short-term interval probability prediction model of photovoltaic power based on the weather similarity clustering and quantile regression neural network （QRNN） monotone model is proposed.Firstly,the monotonicity of QRNN monotone model is introduced in the prediction process to ensure the monotonicity of quantile results,and the Huber norm is used to replace the original loss function,which makes up the defects of the traditional interval prediction of quantile crossing and the non-differentiable loss function.Secondly,based on the data characteristics of meteorological information,the dynamic self-organizing mapping clustering algorithm is used to determine the neuronal neighborhood weight structure through neuronal competition and updating.According to the neighborhood weight,the weather is classified into sunny,cloudy and overcast days,and the data sets under similar weather are obtained.Then,the correlation analysis of the factors affecting the photovoltaic output under different weather conditions is carried out,and the weather influence characteristics under different weather are obtained and input into the neural network as input factors.Finally,the actual data set of Australian Desert Knowledge Solar Energy Center is taken as a case to make interval prediction,and the probability prediction results are given by kernel density estimation,which verifies the reliability of the proposed method.