Non-Intrusive Load Monitoring for Multi-objects in Smart Building

The rapidly expansion of Internet of Things (IoT) has ignited renewed interest in energy disaggregation via non-intrusive load monitoring (NILM). Compared to the more frequent NILM approach of training one model for each appliance, this paper proposes a multi-label learning approach based on the widely cited sequence2point convolutional neural network (CNN). Using the smart meter readings collected in an office building, we demonstrate the accuracy and practicality of the proposed network compared to start-of-the-art one-to-one NILM models.