考虑双尺度相似性的负荷曲线集成谱聚类算法

负荷聚类可以依据形态特性差异对负荷曲线进行归类，实现用户用能行为规律分析，为需求侧响应、电网客户服务等提供重要的决策信息。文中提出一种考虑双尺度相似性的负荷曲线集成谱聚类算法。首先，为了克服欧氏距离在负荷特性相似程度度量上的局限，基于负荷差分向量的余弦距离实现负荷形态变化的相似性度量，提出一种双尺度相似性度量方式；然后，基于双尺度相似性与谱聚类算法，建立差异化基聚类模型；最后，依据聚类评价指标自适应计算基聚类模型权重，以加权一致性矩阵与谱聚类实现聚类集成。算例结果证明，所提方法可有效挖掘负荷形态特性差异，在不同数据集中性能表现稳定，具有显著的聚类有效性和鲁棒性。

Ensemble Spectral Clustering Algorithm for Load Profiles Considering Dual-scale Similarities

Load clustering can help sort out load profiles according to their morphological difference, realize the analysis of users'' energy consumption behaviors, and provide important decision-making information for demand-side response, grid customer service and so on. An ensemble spectral clustering algorithm for load profiles considering dual-scale similarity is proposed. First, in order to overcome the limitation of Euclidean distance in measuring the similarity of load characteristics, the cosine distance of the load difference vector is used to measure the similarity of load shape changes, and a dual-scale similarity measure is proposed. Then, differential basic clustering models are established based on the dual-scale similarity and spectral clustering algorithm. Finally, the weights of basic clustering models are calculated adaptively according to clustering evaluation indices and ensemble clustering is constructed by means of weighted consistency matrix and spectral clustering. Numerical experiments prove that the proposed method can effectively mine the differences in load morphology characteristics and has more stable performance in different datasets, which verifies its good clustering effectiveness and robustness.