Simultaneous disaggregation of multiple appliances based on non-intrusive load monitoring

Non-intrusive load monitoring (NILM) can achieve the disaggregation of power consumption through electricity data at the power entrance without changing the existing circuit structure, which is helpful for saving energy, reducing emission, and improving the utilization of electric energy. This paper studies event-based NILM, which can disaggregate multiple appliances that are switched simultaneously. That is a mixed linear integer programming (MILP) model, where the 0-1 indicates whether the appliances were switched in the event process. And the 0-1 variable constraints of the power feature vectors during the event are constructed. The proposed MILP runs only when events occur, which can reduce the computational complexity. In event detection, non-dominated sorting genetic algorithm II is used to select the parameters of cumulative sum (CUSUM), such as threshold and the length of sliding windows, which realizes the tradeoff between precision and recall. The case studies demonstrate that the improved CUSUM has a precision of 92% in event detection; the proposed event-based NILM approach on the REDD database and laboratory data achieves an accuracy of over 90% on power draws; the appliance with long switching process, such as the inverter air conditioner, can also be monitored by the sequential combination.

Automated summary

This paper studies the event-based NILM approach using a mixed linear integer programming (MILP) model, which can disaggregate power consumption of multiple appliances switched simultaneously. By utilizing non-dominated sorting genetic algorithm II and cumulative sum (CUSUM) technique, a tradeoff between precision and recall can be achieved.