Attention-Enhanced Actor–Critic Learning for Household Nonintrusive Load Monitoring

Nonintrusive load monitoring (NILM) estimates the power of all loads in a smart home, using only the aggregated power on the bus. By leveraging NILM, smart grids implement demand-side management and anomaly diagnosis, optimizing energy allocation, and reducing casualties. To date, some NILM methods have been well developed. However, their estimation accuracy can be further improved when dealing with loads with more than two states. For this reason, a method is proposed using an actor–critic architecture with an attention mechanism to improve accuracy. Both actor and critic are neural networks: the former transforms the aggregated power into estimated load power, while the latter provides feedback on the estimated load power. To capture the dependencies of diverse states, an attention mechanism is equipped to the actor to enhance its representational ability. The proposed method is evaluated on the U.K.-DALE and REDD datasets and demonstrates improved accuracy compared to state-of-the-art.