Sliding-Time-Window and Event-Trigger Based Data Collection Strategy for Non-intrusive Load Monitoring

Non-intrusive load monitoring (NILM) technology can effectively identify the type and operation state of the electrical equipments in the load, so as to help the better analyze the power consumption state and power consumption of users, and achieve better energy saving and emission reduction and load optimization control. However, the large amount of data collected by non-invasive load monitoring terminals, especially high-frequency data, brings challenges to data acquisition, transmission and training of non-intrusive load monitoring models. Hence, in this paper, a sliding-time-window and event trigger based non-intrusive load monitoring data acquisition strategy is proposed, which can select power equipment of state change events through the event trigger mechanism, and fix the length of collect data samples and ensure not lose equipment state change process of context information by using sliding-time-window. Experiments by using widely used non-intrusive load monitoring algorithm evaluation toolkit that named NLIMTK, and widely used public dataset which named REDD, show that the proposed data collection strategy can not only make non-intrusive load monitoring data collected by the size of the largest decreased by 97.07%, and the prediction performance has improved significantly, which fully demonstrate the effectiveness of the proposed method.