Non-intrusive load monitoring using artificial intelligence classifiers: Performance analysis of machine learning techniques

•The use of the electric current signal for the system under analysis.•A methodology for the identification of residential home appliances, through current and active power signals with different sampling.•A comparison between 3 state-of-art artificial intelligence techniques for classifying disaggregation of residential electric loads through the non-intrusive monitoring method.•The presenting of a convolutional neural network that results in higher rates of accuracy compared to previously published studies. In recent years, strategies for load monitoring have been proposed to mitigate power consumption. It has been found, in several reported studies, that as more information is provided for consumers about their electricity consumption, more power energy conservation will occur. In this way, Non-Intrusive Load Monitoring (NILM) has been studied and applied in real-life applications. It consists of detecting and classifying appliances on/off states by measuring electrical signals only at one location of the residential consumer. Several studies have been made using different techniques to improve the accuracy of this strategy. In this paper electromagnetic transients are taking into account and, a performance analysis between cutting-edge artificial classifiers is made. It has been found that 1D convolutional neural networks perform better for this case and electrical current signals are more suitable for NILM, once it carries more features than voltage and power signals.