基于DE-ELM算法的配电网电力系统负荷预测研究

Research on Load Forecasting of Power System for Distribution Network Based on DE-ELM Algorithm

针对目前方法对配电网电力系统进行负荷预测时,由于未能在电力负荷预测前对电力数据进行缺失值插补处理,导致该方法存在预测精度差、时间长以及性能差的问题,提出一种基于DE-ELM(Differential Evolution-Extreme Learning Machine)算法的配电网电力系统负荷预测研究方法。首先依据小波变换对电力数据进行去噪处理,根据去噪结果完成电力数据缺失值的插补,获取完整的电力数据集;再将数据集分成训练集与测试集两部分,将全局寻优引入极限学习机,采用DE-ELM算法对训练集进行计算,依据结果建立网络模型;最后将测试集放入构建的模型中进行训练,基于输出结果实现配电网电力系统的负荷预测。实验结果表明,运用该方法进行配电网电力系统负荷预测时,预测精度高、时长短、性能好。

When the current method is used to predict the load of the power system of the distribution network, because the missing value interpolation processing of the power data is not performed before the power load prediction, the method has poor prediction accuracy, long prediction time. For the problem of poor forecasting performance, a research on the load forecasting of the distribution network power system based on the DE-ELM(Differential Evolution-Extreme Learning Machine) algorithm is proposed. This method first denoises the power data according to the wavelet transform method, completes the interpolation of the missing values of the power data according to the denoising results, and obtains a complete power data set;then divides the data set into two parts: a training set and a test set. The optimization method introduces the extreme learning machine, uses the DE-ELM algorithm to calculate the training set, builds a network model based on the results. Finally puts the test set into the constructed model for training, and realizes the load forecast of the distribution network power system based on the output results. The experimental results show that when the method is used to forecast the load of the distribution network power system, the forecasting accuracy is high, the forecasting time is short, and the forecasting performance is good.