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首页> 外文会议>IEEE Power and Energy Conference at Illinois >Analysis on residential electricity consumption behavior using improved k-means based on simulated annealing algorithm
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Analysis on residential electricity consumption behavior using improved k-means based on simulated annealing algorithm

机译：基于模拟退火算法的改进k均值法分析居民用电行为

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摘要

It is indispensable for electric power companies to classify the electricity consumption behavior of residential customers, in order to understand the user's personalized demands and provide them with targeted services. K-means Clustering algorithm is one of the most popular methods for grouping the consumption patterns in previous studies. However, in traditional K-means algorithm, the initial clustering centroids are selected randomly, which makes it susceptible to local optima and has difficulty in converging to the global minimum. In view of this drawbacks, an improved K-means based on the simulated annealing algorithm is proposed in this paper. By employing the simulated annealing algorithm, the optimal cluster centers can be obtained. By experiments using a large-scale dataset including about 216 houses' consumption records in American, it is shown that the proposed method has a better performance than traditional K-means algorithm and be able to extract typical consumption patterns.
机译：电力公司必须对居民用户的用电行为进行分类，以了解用户的个性化需求并为其提供有针对性的服务。 K-均值聚类算法是先前研究中对消费模式进行分组的最流行方法之一。然而，在传统的K-means算法中，初始聚类质心是随机选择的，这使其容易受到局部最优的影响，并且难以收敛到全局最小值。针对这种缺点，提出了一种基于模拟退火算法的改进的K均值算法。通过采用模拟退火算法，可以获得最优的聚类中心。通过使用包含约216个房屋的消费记录的大规模数据集进行的实验，表明该方法比传统的K-means算法具有更好的性能，并且能够提取典型的消费模式。

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