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The approach detects the load consumption of every device from the aggregated home energy consumption. Most current solutions utilize machine learning (ML) methods resulting in models with many parameters and a high computational load. This article develops an effective feature selection and deep learning-based classification for NILM, which enhances the overall classification accuracy for appliance classification. At first, the data is collected, and the pre-processing stage is performed. Then, the event detection process is performed using the mean absolute deviation-sliding window approach. Then, feature extraction is performed using the Huang–Hilbert transform and time-domain descriptors methods. Then, the most significant features are selected using the new efficient optimization algorithm named Wildebeest herd optimization. Finally, the classification of the individual household appliances is performed using the hybrid wavelet convolutional sparrow search algorithm. The proposed work is simulated in MATLAB using the iAWE dataset. Various evaluation metrics have been computed for the proposed work and are compared to the current methods. Simulation results proved that the proposed method provides better outcomes for appliance classification than other approaches. The proposed model improves the appliance classification accuracy by 3.36%, 3.47%, 8.92%, and 6.38%, respectively, compared to CNN, DNN, KNN, and SVM.

Effective Feature Selection and Deep Learning-Based Classification for Non-Intrusive Load Monitoring