电气化铁路牵引负荷预测研究

Research on traction load forecasting of electrified railway

为保证牵引供电系统电能质量并减少牵引变电所购电成本,文章提出了一种基于稀疏高斯过程(sparse Gaussian process,SGP)的牵引负荷概率预测模型。该模型首先构建以牵引负荷历史特征和时间信息为基础的输入特征向量;然后建立输入特征到牵引负荷之间的映射关系,并用SGP来拟合该映射关系;最后使用滚动预测的形式来实现对牵引负荷的预测。在朔黄铁路某牵引变电所实际运行数据上进行的对比试验验证了所提方法的优越性,其中,点预测可以得到误差在7%左右的预测值,概率预测可以得到不同置信度下可靠的预测区间。

In order to ensure the power quality of traction power supply system and reduce the costs for purchasing electricity by traction substation,a probabilistic forecasting model of traction load based on sparse Gaussian process(SGP)is proposed.Firstly,the model constructed the input feature vector based on the historical characteristics of traction load and time information.Then,the mapping relationship between input characteristics and traction load was established,and SGP was used to fit the mapping relationship.Finally,a rolling forecasting methodology was used to predict the traction load.The comparative experiments carried out on the real operation data of a traction substation on Shuohuang railway verify the advantage of the proposed method,where the point forecasting can obtain the prediction value with an error of about 7%,and the probabilistic forecasting can obtain the reliable prediction interval under different confidence coefficients.