摘要
针对电力系统中长期负荷预测样本少、间隔时间长、影响因素多等问题,提出基于分数阶灰色Elman的组合预测模型,首先针对负荷预测样本少、增长趋势明显的特点,利用分数阶灰色模型弱化原始序列的随机性,降低解的扰动界,其次利用Elman神经网络模型适应性与学习能力强的特点来解决负荷的非线性及影响因素复杂的问题,然后根据最优模型赋予二者最优权值,得到最终的组合模型,最后采用傅里叶级数残差校正模型修正组合模型的误差。仿真结果表明,本文提出的方法可有效拟合负荷的变化趋势,提升负荷预测的准确度。
For the problem of long-term load forecasting in power system with few samples,long interval time and many influencing factors,a combination forecasting model based fractional gray model and Elman neural network was proposed to improve the accuracy.First of all,for the characteristics that the load prediction sample is small and the growth trend is obvious,the randomness of the original sequence was weakened and the perturbation bound of the solution was reduced by the fractional gray model.Secondly,according to the characteristics of the Elman neural network model that it has strong adaptability and learning ability,the problem of the nonlinearity and the complex influencing factors in load were solved by Elman.The final combination model could be given by the optimization model through given the two optimal weights.Finally,the Fourier series residual correction model was used to correct the errors of the combined model.The simulation results show that the proposed method can effectively fit the change trend of load and improve the accuracy of load forecasting.
作者
周步祥
罗燕萍
张百甫
董申
ZHOU Bu-xiang;LUO Yan-ping;ZHANG Bai-fu;DONG Shen(College of Electrical Engineering and Information Technology,Sichuan University,Chengdu 610065,China)
出处
《水电能源科学》
北大核心
2019年第2期192-195,共4页
Water Resources and Power
关键词
分数阶灰色预测
ELMAN神经网络
最优组合模型
傅里叶级数残差校正
负荷预测
fractional-order gray model
Elman neural network
optimal combination model
Fourier series residual correction model
load forecasting
