Abstract
To address the inflexibility of the convolutional autoencoder (CAE) in adjusting the network structure and the difficulty of accurately delineating complex class boundaries in power load data, a particle swarm optimization deep clustering method (DC-PSO) is proposed. First, a particle swarm optimization algorithm for automatically searching the optimal network architecture and hyperparameters of CAE (AHPSO) is proposed to obtain better reconstruction performance. Then, an end-to-end deep clustering model based on a reliable sample selection strategy is designed for the deep clustering algorithm to accurately delineate the category boundaries and further improve the clustering effect. The experimental results show that the DC-PSO algorithm exhibits high clustering accuracy and higher performance for the power load profile clustering.
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