基于信息增益与Spearman相关系数的电力用户行为画像

Power users' behavior portrait based on information gainand Spearman correlation coefficient

随着电力系统新技术的发展以及需求响应等灵活性政策的实施,传统的电力消费者正在逐步转变为产消者,其用电行为习惯也在逐步发生改变。在这一背景下,运用电力用户画像技术可以有效把握电力用户用电特性,挖掘海量用电数据的潜在价值,因此文中提出一种基于信息增益与Spearman相关系数的电力用户行为画像方法。首先,利用基于间隔统计量确定最优聚类数的k-means算法对电力用户用电数据进行聚类分析;然后综合考虑特征有效性与冗余度,构建特征集适应性评价系数;最后采用遗传算法进行迭代求解,得到最优特征子集,对电力用户行为画像进行刻画分析,并通过算例分析验证了所提方法的有效性。

With the development of new technologies in power system and the implementation of flexible policies such as demand response,traditional power consumers are gradually turning into prosumers,and their power consumption habits are also evolving and changing.In this paper,the features of power users and the potential value of massive power consumption data can be described and fully utilized by portrait technology.A method of power users'behavior portrait based on information gain and Spearman correlation coefficient is proposed.Firstly,k-means clustering algorithm based on gap statistic is used to analyze the power users'consumption data.Then,considering the effectiveness and redundancy of the feature set,the adaptability evaluation coefficient is introduced.On this basis,the optimal feature subset is obtained by genetic algorithm.Furthermore,quantitative analysis is implemented to characterize the portrait of power users.Several case studies are presented to demonstrate the effectiveness of the proposed method.