基于ARMA模型的电压RMS值预测

针对配电网节点电压方均根值(root mean square value,RMS)数据规律性差,难以预测的特点,文中提出了一种将自回归移动平均模型(auto-regressive and moving average model,ARMA)应用到电压RMS值预测中的方法。该方法主要包括数据预处理、ARMA模型拟合训练、ARMA模型拟合评价、ARMA模型预测应用4个步骤。运用Python编程语言实现该方法,随机选取两条10 k V等级的电压RMS值监测序列进行ARMA模型拟合训练,并利用训练完成后的模型进行预测分析,结果表明,两条预测序列与实际值的方均根误差分别为9.57和5.05,本文所提方法能够对电压RMS值进行较为有效的预测,具有较好的有效性和实用性。

Prediction of Voltage RMS Value Based on ARMA Model

In light of poor regularity and predictability of RMS value of node voltage in power distribution networks,this paper proposes to apply the ARMA model to predict the RMS value,which mainly comprises of data preprocessing,fitting training of AMRA,fitting review of AMRA,and forecasted application of ARMA.This method is realized through using Python.ARMA fitting training is performed on two randomly selected 10 kV RMS value monitoring sequences,before conducting analysis with the model generated by the training,which demonstrates that root-mean-square errors between the two predicted sequences and actual values are 9.57 and 5.05 respectively.Therefore,method proposed in this paper is applicable in performing voltage RMS value predictions,with reliable effectiveness and practicality.