Particle Swarm Optimization based non-intrusive demand monitoring and load identification in smart meters

Comparing with the traditional load monitoring system, Non-Intrusive Load Monitoring (NILM) system is simple to install and does not need individual sensor for each load. Accordingly, the NILM system can be applied for wide load monitoring and become a powerful energy management and measurement system. Though several NILM algorithms have been developed during the last two decades, the recognition accuracy and computational efficiency remain challenges. To minimize the training time and improve recognition accuracy in Artificial Neural Networks (ANNs), a Particle Swarm Optimization (PSO) is adopted in this paper to optimize parameters of training algorithm in ANN to improve NILM accuracy. Case studies are verified through the combination of Electromagnetic Transients Program (EMTP) simulations and field measurements. The results indicate that the proposed method significantly improves the recognition accuracy and computational speed under multiple operation conditions.