分布式光伏在用户侧的大量接入增加了净负荷预测的难度。为了提高预测精度，提出了一种基于天气分型和神经网络的短期净负荷概率预测模型。首先，为了更好地描述气象条件，将对光伏出力影响最大的云量和环境温度进行加权求和，构造出一种综合的气象影响因子，并提出了一种基于气象影响因子波动率的天气分型方法。同时，针对气象因子对净负荷的影响存在时间差异性，采用最大信息系数帮助模型分段选择输入特征。最后，将样本选择结果作为输入，通过分位数回归和门控循环单元（quantile regression and gated loop unit，QR-GRU）组成的神经网络进行概率预测，并利用核密度估计生成概率密度曲线。通过仿真验证了该模型具有良好的预测性能。

The large number of distributed photovoltaic (PV) access on the user side increases the difficulty of net load forecasting. In order to improve the forecasting accuracy, a short-term net load probabilistic forecasting model based on weather classification and neural network is proposed. First of all, in order to better describe the meteorological conditions, the weighted sum of cloud cover and ambient temperature that have the greatest influence on PV output is carried out to construct a comprehensive meteorological impact factor, and a weather classification method based on the volatility of meteorological impact factor is proposed. At the same time, the maximum information coefficient is used to help the model select the input characteristics in different time frames in view of the time difference of the influence of meteorological factors on the net load. Finally, using the sample selection results as input, probability prediction is made by quantile regression and gated loop unit (QR-GRU) neural network, and probability density curve is generated by kernel density estimation. The simulation results show that the model has good prediction performance.