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电网技术  2010 

中长期负荷预测的异常数据辨识与缺失数据处理

, PP. 148-153

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Abstract:

负荷历史数据是进行中长期负荷预测的基础。历史数据异常及缺失将严重影响负荷预测模型的精度及有效性。针对传统异常数据辨识方法和缺失数据填补方法的不足,提出了基于T2椭圆图的异常数据识别和基于最小二乘支持向量机(leastsquaresupportvectormachine,LSSVM)的缺失数据填补方法。采用偏最小二乘法(partialleastsquare,PLS)提取历史数据主成份,计算各历史样本对主成份的累积贡献率(accumulativecontributionrate,ACR),并绘制T2椭圆,从而识别出历史样本贡献率过大的异常数据。用最小二乘支持向量机拟合历史数据变化趋势,从而实现缺失数据的填补。算例结果表明T2椭圆图能有效识别历史数据中的异常样本;最小二乘支持向量机具有良好的数据填补特性,具有较强的实用价值。

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