β-PruNet: An Explainable, PSO-Optimized Degree-Centrality-Pruned Regularized Extreme Learning Machine for Lightweight...

Accurate recognition of individual appliances from their own power consumption profiles underpins non-intrusive load monitoring (NILM), yet deep learning models often incur high computational overhead and lack operational interpretability. This study proposes a new explainable and lightweight model named β-PruNet to recognize household appliances using daily power consumption signals. The task addressed here is appliance classification from submetered profiles rather than disaggregation of an aggregate household signal, so the model is positioned as the recognition stage of a NILM pipeline. Each 24 h signal (86,400 samples) is transformed into a 720-bin ordinal pattern (OP) distribution, and the distribution is summarized by 27 statistical and spectral descriptors over 48 sliding windows. A compact class-weighted convolutional neural network extracts 128 embedding features. Drawing inspiration from collective swarm intelligence and biological synaptic pruning, a particle swarm optimization (PSO)-tuned regularized extreme learning machine (R-ELM) classifier with beta-weight centrality pruning conducts classification via a diverse softmax ensemble. On an imbalanced 18-class benchmark containing 916 daily appliance profiles, β-PruNet achieved 98.9% classification accuracy and a 97.6% macro F1-score while pruning 87.3% of hidden neurons (from 1500 to 191) without accuracy loss. Post hoc explainability analyses using SHAP, LIME, and Grad-CAM confirmed that the pruning criterion strongly correlates with Shapley feature attribution (r = 0.974). These findings indicate that integrating symbolic dynamics with bio-inspired pruning delivers a computationally efficient, highly accurate, and transparent solution suitable for edge-oriented energy management systems. The theoretical contribution is that the magnitude of a hidden unit's learned output weights provides a usable ranking signal at no additional computational cost beyond the closed-form solution already obtained. The practical contribution is that this signal allows the classifier head to be compressed by 87.3% without a measurable loss in accuracy.