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A particle swarm optimization-based deep clustering algorithm for power load curve analysis
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AI
DOI：10.1016/j.swevo.2024.101650.png)
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Abstract
En 中文
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.
Keywords:
Power load curve
Particle swarm optimization
Deep clustering algorithm
Load feature extraction
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