摘要
灰色理论在电力负荷预测领域中有重要应用,为了扩展灰色模型在中长期电力负荷预测中应用,提出了一种基于加权马尔可夫优化的非线性灰色伯努利(nonlinear grey Bernoulli Model,NGBM(1,1))预测模型.首先引进新型非线性NGBM(1,1)模型对电力负荷数据的总体趋势进行拟合,得到的灰拟合精度序列是一个随机波动的过程,再利用加权马尔可夫的特点确定灰拟合精度的加权转移概率矩阵,通过插值和还原计算对NGBM(1,1)模型的预测结果进行优化.将该模型运用到江苏省农村电力负荷预测中,结果验证其在预测精度上的优越性,并用于中长期电力负荷预测是有效可行的.
Grey theory is one of the important methods of medium and long term power load forecasting.In order to improve the accuracy of the grey model in power load forecasting,a prediction mode of nonlinear grey Bernoulli model(NGBM(1,1))based on weighted Markov optimization is proposed in this paper.Firstly,the new nonlinear NGBM(1,1)model is used to fit the general trend of the power load data,at its grey error index sequence abtained is a random fluctuation process.Then,the weighted transfer probability matrix of grey error index is determined by the advantage of weighted Markov,and the prediction result of NGBM(1,1)model can be optimized by interpolation calculation.Finally,this model is applied to rural power load forecasting in Jiangsu Province,and the results verify its superiority in prediction accuracy.This model is effective and feasible for medium and long term power load forecasting.
作者
刘嘉
王泽滨
LIU Jia;WANG Zebin(School of Mechanical Engineering,Zhejiang University,Hangzhou 264200,China;Shandong Senter Electronics Co.Ltd.,Zibo 255000,China)
出处
《江苏科技大学学报（自然科学版）》
CAS
2019年第5期60-66,共7页
Journal of Jiangsu University of Science and Technology:Natural Science Edition
