基于改进灰色模型的中长期电力负荷预测

在中长期负荷预测中,由于电力负荷具有突变性,常规GM(1,1)模型不能及时反映负荷的突然变化,对于发生转折的数据预测精度不高,在实际应用中具有一定的局限性。通过对负荷原始数据序列的预处理及优化,利用等维新息递推GM(1,1)模型进行预测,保证了预测能够较为充分地利用新信息,并加入了残差模型,通过实例分析表明,改进后的模型比常规GM(1,1)模型提高了预测的精度。

Gray Model Based on Improved Medium and Long Term Load Forecasting

In the medium and long term load forecasting,due to the mutation load,the conventional GM(1,1) model can not reflect the sudden changes of load in time,then the shift prediction accuracy of the data is not high,in practice having some limitations.By the pretreatment and optimization of the original load data sequence,the use of an innovation recursive GM(1,1) model to forecast,ensuring that new information can be utilized more fully for the prediction,and joined the residual model,the example shows that the improved model than conventional GM(1,1) model improves the prediction accuracy.