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Palabras clave – provistas por la editorial

smart grid; nontechnical losses; electricity theft detection; synthetic minority oversampling technique; K-means cluster; random forest; smart grids; smart energy system; smart meter; GDPR; data privacy; ethics; multi-label learning; Non-intrusive Load Monitoring; appliance recognition; fryze power theory; V-I trajectory; Convolutional Neural Network; distance similarity matrix; activation current; electric vehicle; synthetic data; exponential distribution; Poisson distribution; Gaussian mixture models; mathematical modeling; machine learning; simulation; Non-Intrusive Load Monitoring (NILM); NILM datasets; power signature; electric load simulation; data-driven approaches; smart meters; text convolutional neural networks (TextCNN); time-series classification; data annotation; non-intrusive load monitoring; semi-automatic labeling; appliance load signatures; ambient influences; device classification accuracy; NILM; signature; load disaggregation; transients; pulse generator; smart metering; smart power grids; power consumption data; energy data processing; user-centric applications of energy data; convolutional neural network; energy consumption; energy data analytics; energy disaggregation; real-time; smart meter data; transient load signature; attention mechanism; deep neural network; electrical energy; load scheduling; satisfaction; Shapley Value; solar photovoltaics; review; deep learning; deep neural networks; n/a