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METHOD OF PREDICATING ULTRA-SHORT-TERM WIND POWER BASED ON SELF-LEARNING COMPOSITE DATA SOURCE

机译:基于自学习复合数据源的超短期风功率预测方法

摘要

A method of predicating ultra-short-term wind power based on self-learning composite data source includes following steps. Model parameters of an autoregression moving average model are obtained by inputting data. A predication result is obtained by inputting data required by wind power predication into the autoregression moving average model. A post-evaluation is performed to the predication result by analyzing error between the predication result and measured values, and performing model order determination and model parameters estimation again while the error is greater than an allowable maximum error.
机译:一种基于自学习复合数据源的超短期风能预测方法,包括以下步骤。通过输入数据获得自回归移动平均模型的模型参数。通过将风能预测所需的数据输入到自回归移动平均模型中来获得预测结果。通过分析预测结果和测量值之间的误差,并在误差大于允许的最大误差的同时再次执行模型顺序确定和模型参数估计,对预测结果执行后评估。

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