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中文摘要：
在混沌局域预测算法中，通常使用欧氏距离和关联度衡量相点间的相关性，以确定参考邻域，但这些参量不能有效衡量相点相关性。在极坐标系下，将相点视为向量，则相点间相关性应由向量间的模比值和向量夹角共同确定。根据以上思路，将相点向量的模比值和夹角余弦值融合为一个衡量相点相关性的参量，以确定参考邻域。在预测参数识别中，以相点间的模和夹角作为优化目标，提出了新的线性拟合参数求解算法。将以上算法应用到某城市的电力负荷短期预测中，获得了令人满意的预测效果。
英文摘要：
In chaotic local forecasting algorithm, Euclid distance and correlation degree are usually used to measure correlativity between phase points to decide the reference neighborhood, however the correlativity of phase points cannot be effectively measured by these parameters. In polar coordinate system the phase points are regarded as vector, in that way the correlativity between phase points should be jointly determined by the ratio of modules between vectors and the included angle of vectors to decide reference neighborhood. According to above idea, the ratio of modules between vectors and the cosine of included angle of vectors are amalgamated into a parameter to measure the correlativity between phase points to decide reference neighborhood. In forecasting parameter identification, the modules and included angles between phase points are taken as optimization objective, then a new algorithm to solve linear fitting parameter is proposed. Applying the proposed algorithm to short-term urban load forecasting, satisfied forecasting results are obtained.
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