The penetration of distributed photovoltaics on the user side increases the fluctuation of the net load and makes it harder to forecast. To improve forecasting precision, a short-term net load hybrid forecasting model is proposed, which considers weather classification and neural network. First, a weather conditional factor is constructed to describe weather conditions. A k-means algorithm based on the volatility of weather conditional factor (VW-KA) is put forward for weather classification. Besides, due to the temporal differences in the impact of weather factors on the net load, the maximal information coefficient is used to choose the input features by segmented selection. Then, a hybrid neural network consisting of quantile regression and convolutional neural network-gated recurrent unit (QR-CNN-GRU) utilizes the results of the sample selection stage as inputs to perform hybrid prediction, and generated the probability density curves by kernel density estimation. The superiority of the model is validated by comparing the obtained results with other models.

中文翻译：

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基于VW-KA和QR-CNN-GRU的短期净负荷混合预测方法

分布式光伏在用户侧的渗透增加了网络负荷的波动性，增加了预测的难度。为了提高预报精度，提出了考虑天气分类和神经网络的短期净负荷混合预报模型。首先，构建天气条件因子来描述天气状况。提出一种基于天气条件因子波动性的k均值算法(VW-KA)进行天气分类。此外，由于天气因素对净负荷的影响存在时间差异，因此采用最大信息系数来分段选择输入特征。然后，由分位数回归和卷积神经网络门控循环单元（QR-CNN-GRU）组成的混合神经网络利用样本选择阶段的结果作为输入进行混合预测，并通过核密度估计生成概率密度曲线。通过与其他模型的比较得到的结果验证了该模型的优越性。