Particle-Swarm-Optimization-Based Nonintrusive Demand Monitoring and Load Identification in Smart Meters

Compared with the traditional load monitoring system, a nonintrusive load monitoring (NILM) system is simple to install and does not need an 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. Although several NILM algorithms have been developed during the last two decades, recognition accuracy and computational efficiency remain as challenges. To minimize training time and improve recognition accuracy, particle swarm optimization is adopted in this paper to optimize parameters of training algorithms in artificial neural networks. The proposed algorithm is verified through the combination of Electromagnetic Transients Program simulations and field measurements. The results indicate that the proposed method significantly improves recognition accuracy and computational efficiency under multiple operation conditions.