Abstract
Residential load profiling can support the design of differentiated demand-side management, but clustering methods are sensitive to representation quality and initialization. We developed a profiling workflow that combines a convolutional attention variational autoencoder (CA-VAE), adaptive gray wolf optimizer K-Means (GWO-K-Means) search over candidate cluster numbers and centers, and a clustering-loss refinement stage. On 6390 prepared residential average-day load profiles from the Commission for Energy Regulation dataset, ten independent runs yielded a final silhouette coefficient of 0.5002 (SD 0.0314), a Davies–Bouldin index of 0.8532 (SD 0.0383), and a Calinski–Harabasz index of 3694.3 (SD 260.5). Relative to raw K-Means, the final workflow improved all three internal validity measures (paired Wilcoxon signed-rank test, p = 0.001953 for each metric). GWO selected K = 5 in 6 of 10 runs; this modal solution produced five interpretable load profiles. The profiles provide load-shape-derived candidate groups for future demand-response trials, rather than validated estimates of operational flexibility.