基于卷积变分自编码器的日负荷曲线聚类研究
摘要：用户日用电数据可以反映用户的用电行为特征，聚类任务能够从大量运行数据中提取典型用户日负荷曲线为电力系统的规划与调度等任务提供依据。针对传统聚类方法在数据量庞大、数据维度较高的日负荷数据场景中具有效率低下、提取潜在表征困难等问题，提出基于卷积变分自编码器(variational autoencoders，VAE)的聚类方法对负荷曲线进行聚类。该方法首先通过卷积变分自编码器降维提取日负荷数据的潜在特征，并配合K-means进行负荷聚类任务，最后基于各负荷曲线与聚类中心的距离通过加权修正每一类聚类中心以得到更具代表性的典型日负荷曲线。利用UCI数据集中的葡萄牙用户实际采集数据进行算例验证，结果显示该方法的戴维森堡丁指数(Davies-Bouldin Index, DBI)相较于传统聚类方法K-means、PCA+K-means等下降明显，说明类内更加紧密，类间更加远离，提高了聚类质量。然后利用高斯距离加权改进了聚类中心，提取到更加典型日负荷曲线，使得分析用户用电行为特征更为精确。验证了卷积变分自编码器聚类方法在日负荷曲线中的有效性。
Abstract:The users' daily electricity consumption data can reflect their electricity consumption behavior characteristics, and the clustering task can extract users' representative daily load from a large amount of operating data, it can provide such a basis for tasks as power system planning and scheduling. In allusion to such defects in traditional clustering methods as low efficiency and the difficulty in extracting potential representations from daily load data scenarios with huge data size and high data dimensions, a clustering method based on convolutional-variational autoencoder (C-VAE) was proposed to cluster the load curves. Firstly, the potential characteristics of daily load data was extracted by dimensionality reduction of C-VAE. Secondly, cooperated with K-means the load clustering task was conducted. Finally, based on the distance between each load curve with the clustering center, each sort of clustering center was revised by weighted correction to obtain more representative typical day load curve. The actually collected data from Portuguese users in UCI dataset was utilized to conduct the examples validation, and the results show that the Davies-Bouldin Index (DBI) of this method decreased significantly than such traditional clustering methods as K-means, PCA+ K-means and so on, it also showed that the data within each type was more compact, and the distance among each type was further away, so the clustering quality was improved. Afterward, the Gaussian distance weighting was utilized to improve the clustering center, and a more typical daily load curve was extracted to make the analysis on the characteristics of users' electricity consumption behavior more accurate. Thus, the effectiveness of the convolutional variational autoencoder clustering method in daily load curve clustering task was verified.
标题：基于卷积变分自编码器的日负荷曲线聚类研究
英文标题：Clustering Analysis of Daily Load Curve Based on Convolutional Variational Autoencoder
作者：杨亚兰, 李晨鑫, 秦毓毅, 王渝红, 方飚, 舒虹,
关键词：负荷聚类, 典型日负荷曲线, 卷积神经网络(CNN), 变分自编码器(VAE), 距离加权,
发表日期：2023-09-26
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