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针对传统GM(1,1)模型在处理振荡序列时预测模拟精度不高,部分改进模型计算复杂度高、数据需求大、泛化能力弱等问题,从数据序列特征的角度提出一种振荡序列灰色预测方法。观察分析振荡序列呈现的序列特征和发展趋势,利用序列变动趋势将数据分组截取部分近期数据序列、利用季节特性将序列分组3类规则对数据进行预处理,再根据分组数据序列特征利用缓冲算子和GM(1,1)模型对振荡序列进行预测。通过多案例实证分析,验证该方法在预测模拟性能方面明显优于传统GM(1,1)模型及部分改进模型,并且提高了GM(1,1)模型的泛化能力,且该方法简单易于操作。将该方法应用于河南省太阳能发电量的预测中,验证了方法的有效性。

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