分布式能源2026，Vol.11Issue(1)：54-62,9.DOI:10.16513/j.2096-2185.DE.25100096
面向配电网典型负荷的卷积自编码器特征提取与标签优化方法
A Convolutional Autoencoder-Based Approach for Feature Extraction and Label Optimization of Typical Loads in Distribution Networks
摘要
Abstract
Extracting the latent value embedded in electricity load data constitutes one of the key challenges in the power industry.To address the difficulty faced by conventional clustering approaches in capturing the intrinsic features of high-dimensional load data,this paper proposes an optimized clustering method based on a one-dimensional convolutional autoencoder(1D-CAE).First,a 1D-CAE is employed to extract temporal features from daily customer load profiles through nonlinear dimensionality reduction,with the objective of minimizing reconstruction loss.Second,we introduce an improved Cayley orthogonal constraint to enhance the structural information of the clustering space,thereby optimizing the mapping of latent features and improving clustering stability.Third,a generative adversarial network(GAN)is integrated with K-means clustering to refine the cluster centers and fine-tune the encoder.Finally,the effectiveness of the proposed method is evaluated on real-world load datasets using three widely accepted internal validation metrics:the Davies-Bouldin index(DBI),the Calinski-Harabasz index(CHI),and the silhouette coefficient(SC).Experimental results demonstrate that the proposed approach significantly enhances both inter-cluster separability and intra-cluster compactness.The study confirms that the method can effectively identify and extract morphological characteristics of diverse load profiles,offering robust support for demand response and optimal dispatch in virtual power plants.关键词
负荷聚类/自编码器/负荷特性/卷积神经网络Key words
load clustering/autoencoder/load characteristics/convolutional neural network分类
能源科技引用本文复制引用
李佳宇,杨家星,苗桂喜,王鑫,元亮,贾学法,马辉..面向配电网典型负荷的卷积自编码器特征提取与标签优化方法[J].分布式能源,2026,11(1):54-62,9.基金项目
国家自然科学基金项目(52377191) （52377191）
湖北省自然科学基金面上项目(2024AFB584) This work is support by National Natural Science Foundation of China(52377191) （2024AFB584）
Natural Science Foundation of Hubei Province(2024AFB584) （2024AFB584）
