摘要
本文针对电力负荷变化的非平稳性和周期性，采用灰色模型，可调灰色模型分析用电负荷的趋势项并与历史负荷比较得一系列残差，然后应用自回归模型，傅氏模型，人工神经网络模型进行修正以提高精度。用一系列组合模型分别用于不同场合和要求下的负荷预测，并在微机上开发软件，通过实例计算，效果良好。
In view of the non-stationarity and periodically of electric load, the auther first sets up the grey model or adjustable grey model for the analysis of the load trend. By comparing grey model values with the original ones, the auther gets a series of errors,then by means of the secondary data, the auther further applies autoregressive model, fourier model or artificial neural network model to the adaptability modification of the errors resulting from the grey model. Taking as an example show that these methods is efficient. The forecasting accuracy is more accurate than that of conventional methods.
出处
《系统工程理论与实践》
EI
CSCD
北大核心
1996年第7期99-105,共7页
Systems Engineering-Theory & Practice
关键词
电力负荷
预报
灰色模型
神经网络
fourier model
artificial neural network modelcombinational model
