基于卷积自编码器的日负荷深度嵌入聚类方法

doi:10.12204/j.issn.1000-7229.2021.01.015

电力建设 ›› 2021, Vol. 42 ›› Issue (1): 132-138.doi: 10.12204/j.issn.1000-7229.2021.01.015

• 智能电网 • 上一篇

基于卷积自编码器的日负荷深度嵌入聚类方法

黄冬梅¹, 林孝镶², 胡安铎¹, 孙锦中¹

1.上海电力大学电子与信息工程学院,上海市 200090
2.上海电力大学电气工程学院,上海市 200090

收稿日期:2020-07-27 出版日期:2021-01-01 发布日期:2021-01-07
通讯作者: 胡安铎
作者简介:黄冬梅(1964),女,教授,博导,主要研究方向为电力与海洋时空信息技术;|林孝镶(1996),男,硕士研究生,主要研究方向为电力负荷数据分析;|孙锦中(1980),男,硕士,讲师,主要研究方向为电力时空信息技术。
基金资助:
中国极地研究中心项目(H2019-203);上海市科委地方院校能力建设项目(20020500700)

Deep Embedding Clustering Method for Daily Load Based onConvolutional Auto-Encoder

HUANG Dongmei¹, LIN Xiaoxiang², HU Anduo¹, SUN Jinzhong¹

1. College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 200090, China
2. College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China

Received:2020-07-27 Online:2021-01-01 Published:2021-01-07
Contact: HU Anduo

摘要/Abstract

摘要：

负荷聚类是电力大数据分析的重要基础。针对高维日负荷数据时序特征提取困难,以及特征提取与聚类处理分离降低负荷聚类准确性的问题,文章提出了一种基于一维卷积自编码器的日负荷深度嵌入聚类方法(deep embedding clustering method based on one dimensional convolutional auto-encoder,DEC-1D-CAE)。首先,采用一维卷积自编码器网络提取负荷曲线蕴含的时序特征。然后,利用自定义聚类层对所提取的负荷特征向量进行软划分。最后,采用KL散度(Kullback-Leibler divergence,KLD)为损失函数,联合优化卷积自编码器与聚类层,得到聚类结果。算例分析表明所提方法在DBI(Davies-Bouldin index)、CHI(Calinski-Harabasz index)指标上均优于K-means、1D-CAE+K-means、基于堆叠式编码器的深度嵌入聚类方法(deep embedding clustering method based on stacked auto-encoder,DEC-SAE),所提方法可以有效提升日负荷聚类的准确性。

关键词: 负荷聚类, 卷积自编码器(CAE), 深度嵌入聚类方法(DEC), 时序特征提取

Abstract:

Clustering of load data is an important foundation for analyzing electrical big data. Aiming at the difficulty of extracting sequential features of high-dimensional daily load data, and the reduction of accuracy of load clustering due to the separation of feature extraction and clustering processing, a deep embedding clustering method based on one dimensional convolutional auto-encoder (DEC-1D-CAE) is proposed for daily load data in this paper. Firstly, a one-dimensional convolutional auto-encoder is used to extract sequential features contained in the load curve. Then, a user-defined clustering layer is used for soft division of the extracted load feature vector. Finally, the Kullback-Leibler divergence (KLD) is used as loss function to jointly optimize convolutional auto-encoder and the clustering layer to obtain the clustering result. A numerical experiment were carried out and the results of the proposed method are better than K-means, 1D-CAE+K-means and DEC-1D-CAE on both Davies-Bouldin index (DBI) and Calinski-Harabasz index (CHI), which indicate that the proposed method can effectively improve the accuracy of daily load clustering.

Key words: load clustering, convolutional auto-encoder(CAE), deep embedding clustering method (DEC), sequential feature extraction

中图分类号:

TM73

黄冬梅, 林孝镶, 胡安铎, 孙锦中. 基于卷积自编码器的日负荷深度嵌入聚类方法[J]. 电力建设, 2021, 42(1): 132-138.

HUANG Dongmei, LIN Xiaoxiang, HU Anduo, SUN Jinzhong. Deep Embedding Clustering Method for Daily Load Based onConvolutional Auto-Encoder[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(1): 132-138.

图/表 7

图1 1D-CAE模型结构

Fig.1 Structure of 1D-CAE model

图2 基于一维卷积自编码器的日负荷深度嵌入聚类方法流程

Fig.2 Flowchart of deep embedding clustering method of daily load applying one-dimensional convolutional auto-encoder

图3 负荷序列重构对比

Fig.3 Comparison of load sequence reconstruction

图4 负荷重构相对误差

Fig.4 Relative error of load reconstruction

图5 本文方法聚类结果

Fig.5 Clustering results of the proposed method

图6 K-means算法聚类结果

Fig.6 Clustering results of K-means algorithm

表1 4种聚类方法指标对比

Table 1 Comparison of four clustering methods

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基于卷积自编码器的日负荷深度嵌入聚类方法

Deep Embedding Clustering Method for Daily Load Based onConvolutional Auto-Encoder

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