电力短期负荷的混沌局域关联性预测

在混沌局域预测中,相空间最近邻域点的确定通常采用欧氏距离法,其预测精度在很大程度上取决于所确定的最近邻域点性态,然而距离最近并不一定意味着预测效果最好,当该邻域存在伪近邻点或系统具有高嵌入维数时某些邻域点的演化轨迹在一步或多步后会远离预测点,究其原因是欧氏距离难以反映最邻近点与预测状态的关联程度.因此,作者提出了将欧氏距离和关联度相结合的思想,并将该方法应用于电力短期负荷预测,结果显示该方法能有效地提高预测精度.

Local Nonlinear Forecasting of Short-term Power Load Forecasting

The nearest points in phase space are determined by Euclid distance in chaotic local prediction. The prediction accuracy depends on quality of the nearest points. But the shortest distance does not imply better forecasting effect. While false nearest neighboring point or high embedding dimensions appear evolvement track of some nearest neighboring point should be apart from prediction point. Because it is difficult for Euclid distance to reflect the correlation degree between the nearest points and prediction point. So the idea of combining Euclid distance with correlation degree is put forward. The method is applied to short-term electrical load forecasting. The result of load series forecasting by the presented method is more effective to improve prediction accuracy.