As the multi-daily electricity consumption behaviors have the strong characteristics of dynamicity, nonlinearity and locality caused by temporal manifold structure, the existing methods are difficult to fine-grained and accurately classify it. To solve this problem, this paper proposes a hierarchical classification method based on the temporal extension of the neighborhood preserving embedding algorithm (TNPE) and Bayes. The input data are multi daily-load curves of a single consumer, including power-hour-day three dimensions, which contains the full information of the user’s consumption behaviors not only in hours, but also in days. Firstly, electricity consumption behaviors are divided into routine and non-routine types by k-means clustering algorithm. Secondly, the load feature mapping matrix of different industries is extracted through the TNPE, and each TNPE model can regard as one binary classifier, so the multi-classifier is constructed through multiple TNPE models. Finally, by converting the feature similarity between samples into probabilities, a Bayesian model is established to realize which the power consumption type belongs to. The case results show that this method can effectively recognize the local dynamic features in the temporal load data, and obtain a higher classification accuracy through a smaller number of training samples.

由于日常用电行为具有由时间流形结构引起的强烈的动态性，非线性和局部性的特点，因此现有方法难以细化和准确分类。为了解决这个问题，本文提出了一种基于邻域保留嵌入算法（TNPE）和贝叶斯算法的时间扩展的分层分类方法。输入数据是单个用户的多条日负荷曲线，包括电-小时-日三个维度，其中不仅包含小时数，而且还包含天数用户的消费行为的完整信息。首先，用k将耗电量行为分为常规和非常规类型-均值聚类算法。其次，通过TNPE提取不同行业的负荷特征映射矩阵，每个TNPE模型都可以看作一个二元分类器，因此通过多个TNPE模型构造多分类器。最后，通过将样本之间的特征相似度转换为概率，建立贝叶斯模型以实现功耗类型所属。实例结果表明，该方法可以有效地识别时间负荷数据中的局部动态特征，并通过较少的训练样本获得较高的分类精度。