【摘要】 为了提高电力负荷曲线聚类精度,文中提出了一种基于核主成分分析(KPCA)和改进K-means算法的电力负荷曲线聚类方法。该方法首先在划分聚类算法K-means基础上融入密度聚类思想,提出了融合密度思想的K-means算法(DK-means算法),并在电力负荷曲线实验集上对比分析其聚类效果;接着在实验集上比较各种降维算法的降维聚类精度和降维速度;最后分析KPCA+DK-means组合算法的降维聚类能力。结果表明,戴维森堡丁指数(DBI)更适合作为电力负荷曲线聚类评价指标;以DBI为评价指标,与K-means、BIRCH、DBSCAN和EnsClust 4种聚类算法相比,DK-means的聚类精度更高;与LLE、MDS、ISOMAP 3种非线性降维算法相比,KPCA的降维速度更快;KPCA+DK-means组合算法有良好的降维聚类能力,较DK-means在聚类精度和聚类效率上均有提升。KPCA+DK-means组合算法可以实现电力负荷曲线的高效降维、精确聚类,对用电行为模式的准确提取起关键技术支持作用。更多还原

【Abstract】 A clustering method of power load profiles based on kernel principal component analysis(KPCA) and improved K-means algorithm was proposed to improve clustering accuracy of power load profiles. Firstly, a K-means algorithm based on density idea, namely, density K-means(DK-means) was proposed by combining the density clustering method with partitioning clustering algorithm K-means. And the clustering effect was comparatively analyzed on the experimental set of power load profiles. Then the dimensionality reduction accuracy and speed of various dimensionality reduction algorithms were compared on the experimental set. Finally, the dimensionality reduction and clustering ability of the KPCA+DK-means combination algorithm was analyzed. The results show that, firstly, Davies-Bouldin Index(DBI) is more suitable for the evaluation index of power load profiles clustering; se-condly, taking DBI as the evaluation index, the clustering accuracy of DK-means is higher than that of K-means, BIRCH, DBSCAN and EnsClust algorithms; thirdly, compared with LLE, MDS and ISOMAP, the dimensionality reduction speed of KPCA is faster; finally, the KPCA+DK-means combination algorithm has good dimensionality reduction and clustering ability, and its clustering accuracy and clustering efficiency are better than those of DK-means. In short, the KPCA+DK-means combination algorithm can achieve efficient dimensionality reduction and accurate clustering of power load profiles, thus plays a key technical role in accurately extracting information of electricity consumption behavior.更多还原