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中文摘要：
将偏最小二乘方法应用于年度负荷预测,可以利用有限容量样本剔除冗余信息,建立线性回归方程,但其对随机因素影响的相对重要性提升会降低预测精度。根据年度负荷以及主要影响因素的趋势变化特点,采用灰色模型对其进行模拟,以经验风险最小的预测值代替原始数据进行偏最小二乘建模,从而削弱随机因素的影响,提高预测精度。试验证明该方法有效可行。
英文摘要：
The Partial Least-Square method utilizes limited samples to reject redundant information and establish linear regression equation. However since more importance is attached to the random factors, the forecasting accuracy may be affected. According to the changes of annual load and its related factors, the author adopts grey model to simulate annual load and proposes the partial least-square regression model by using predicative value of minimum practical risk, instead of original data. Thus the influence of random factors could be reduced and accuracy is improved. Experiments show that this method is effective and practical.
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Research on the wavelet neural networks based on data mining and its application in daily load forec
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