基于用电行为数字特征画像的电力用户两阶段分类方法

Two-Stage Power User Classification Method Based on Digital Feature Portraits of Power Consumption Behavior

对用户开展精细化用电行为画像及分类,是电力企业精准掌握用户用电规律、提升服务水平和市场竞争力的关键因素之一。针对当前电力用户分类研究中用户用电行为画像结果片面、集成学习负荷分类研究中的基分类器冗余问题及负荷类别不平衡问题,提出一种基于用电行为数字特征画像的电力用户两阶段分类算法。第一阶段,提出一种结合谱聚类和集成强基分类器的用户日负荷曲线分类算法:首先,针对集成学习基分类器学习能力弱的不足,提出一种基于改进长短期记忆网络(long short-term memory,LSTM)的强基分类器;其次,针对基分类器冗余问题,提出一种基于最小正则化代理经验风险的优化选择集成策略;然后,提出一种基于密度的高斯过采样方法处理类别不平衡。第二阶段,基于负荷曲线分类结果,构建以日负荷模式发生概率为数字特征的用户用电行为画像,采用谱聚类算法对用户画像实施分类。最后,通过实测用户负荷数据验证了所提方法的有效性。

Fine power consumption behavior portrait and classification has been one of the key factors for power enterprises to accurately grasp the electricity consumption law of power consumers,improve service level and market competitiveness.To solve the issues of fragmentary portraits of electricity consumption behavior in current power user classification research,base classifier redundancy and class imbalance in ensemble learning load classification,a two-stage power consumer classification method based on digital feature portraits of power consumption behavior is proposed.In the first stage,a classification method for power user daily load curves is proposed combining spectral clustering and integrated strong base classifier.Firstly,a strong base classifier is developed on the basis of LSTM network to improve the weak learning capability of base classifier in ensemble learning.Secondly,an Optimal Selection Ensemble(OSE)strategy based on minimum regularized surrogate empirical risk is proposed to solve the problem of base classifier redundancy.Thirdly,a Density Based Gaussian Synthetic(DBGS)minority over-sampling technique is proposed for class imbalance.In the second stage,the power consumption behavior portraits with daily load-pattern occurrence probability as the digital features are constructed,and the portraits are classified by spectral clustering.Finally,the effectiveness of the proposed method is verified by the measured user load data.