Monitoring and Identification Electricity Load Using Artificial Neural Network

This study proposes a model that can be used for monitoring and identifying consumer electrical loads using Neural Network and Feed-Forward Backpropagation training methods. The modeling design is made in Simulink-Matlab software. The neural network training error obtained is 2.729%, then this is used for the identification process of electrical loads, which in this study used 21 load combinations. While the error test results obtained are 1.325476%. In this study, a new model is proposed for monitoring and identifying consumer electricity consumption by applying the concept of Non-Intrusive Load Monitoring (NILM) combined with the Artificial Neural Network (ANN) method.