基于神经网络的配电网负荷预测方法

配电网接入高渗透率分布式光伏在一定程度上削减了配电网负荷。由于配电网负荷、光伏出力与气象因素耦合特性存在差异，且均具有较强随机性，致使配电网净负荷预测难度大、随机性高。为实现波动性配电网短时预测净负荷，基于长短期记忆（LSTM），构建神经网络短期预测模型。通过LSTM构建光伏出力短期预测模型及小时前配电网负荷预测模型，并采用交叉验证，对各LSTM预测器结构超参数进行优化。将两者预测结果进行比较，得到配电网净负荷。由实验结果分析可知，LSTM方法可自适应挖掘光伏出力特征、历史负荷预测对象之间的相关性，较支持向量回归（SVR）方法，该方法预测精度高、过程简单。

Load Forecasting Method of Distribution Network Based on Neural Network

The distribution network connected to high permeability distributed photovoltaic should reduce the load of distribution network to a certain extent. Due to the difference of the coupling characteristics of the load, photovoltaic output and meteorological factors in the distribution network, and the strong randomness, it is difficult and randomness to predict the net load of the distribution network. In order to realize the short-term prediction of the net load of the fluctuating distribution network, the short-term prediction model of the neural network is constructed based on the long-short term memory (LSTM). The short-term prediction model of photovoltaic output and the hourly load prediction model of distribution network are built by LSTM, and cross validation is used to optimize the structure super parameters of each LSTM predictor. The net load of distribution network is obtained by comparing the predicted results. From the analysis of the experimental results, it can be seen that the LSTM method can adaptively mine the correlation between photovoltaic output characteristics and historical load forecasting objects. Compared with the support vector regression (SVR) method, this method has high prediction accuracy and simple process.