电力信息与通信技术2024，Vol.22Issue(7)：35-44,10.DOI:10.16543/j.2095-641x.electric.power.ict.2024.07.05
基于DAE和改进RFKM的负荷数据精准特征提取与标签定义
Accurate Feature Extraction and Label Definition of Load Data Based on DAE and Improved RFKM
摘要
Abstract
Aiming at the problems of difficult feature extraction of high-dimensional time series data of user load data,difficult cross-data clustering processing,and difficult accurate labeling of load data in distribution network,this paper proposes a feature extraction and label definition model based on DAE and improve RFKM (FLMbD-iR) for user load data based on denoising autoencoder and improved rough fuzzy K-means. After the deep feature extraction of the original user load data by the denoising autoencoder,FLMbD-iR uses the rough fuzzy K-means based on the imbalanced measure of cluster sizes,and deals with the error of the cross data between clusters in the clustering. Finally,the description index is constructed to label the typical daily load curve. The experiment uses the American power load data for simulation. The experimental results show that this method has a significant effect on the clustering processing of user load data.关键词
负荷聚类/降噪自编码器/粗糙模糊K-means聚类/类簇规模不均衡度量/精准特征提取Key words
load clustering/denoising autoencoder/rough fuzzy K-means clustering/imbalanced measure of cluster sizes/accurate feature extraction分类
信息技术与安全科学引用本文复制引用
刘礼,杨佳轩,强仁,龚钢军,陆俊,武昕..基于DAE和改进RFKM的负荷数据精准特征提取与标签定义[J].电力信息与通信技术,2024,22(7):35-44,10.基金项目
国家重点研发计划项目(2022YFB3105101). （2022YFB3105101）
