A Study of the Effects of Appliance Energy Signatures on Different Neural Network Types in Nonintrusive Load Monitoring

Access to appliance-level energy measurements enables end users to take conservative actions. The availability of energy data and smart energy meters has recently increased the popularity of nonintrusive load monitoring (NILM). Both end users and utilities can estimate appliance-level energy consumption data using NILM without installing appliance-level sensors. Neural network (NN)-based NILM models have shown promising results compared to other energy disaggregation approaches. The estimation accuracy depends on the NN type and architecture selected for the NILM application. The varying characteristics and operating parameters of different types of appliances make a substantial impact on the NILM performance. Therefore, it is essential to study the effects of appliance signatures on different NN architectures to select the optimum NN type and efficient energy disaggregation. This study comprehensively investigates the impact of various parameters specific to household environments and appliances on disaggregation performance. The NN models used in this study were modified and improved for comparison on a common platform. Subsequently, a rigorous comparison was performed using disaggregation performance indices, the training time of NNs, and computational complexity. The results show that the disaggregation performance depends on the types of NN, network hyperparameters, appliance activation signatures, and the availability of training data volume. The findings of this study demonstrate that the convolutional NN (CNN) outperforms other NNs for NILM applications. In addition, the specific characteristics of individual appliances have a notable impact on disaggregation performance.