基于DAE和改进RFKM的负荷数据精准特征提取与标签定义OACSTPCD

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.