An Efficient Mixed-Integer Programming Model for Nonintrusive Load Monitoring With Distributionally Robust Feature Extraction

For the nonintrusive load monitoring problem, we propose a new efficient mixed-integer linear programming model. Compared to other optimization-based models that are only capable of distinguishing between the on/off states of appliances, our model takes it a step further by incorporating continuous power variables and constructing power feature constraints, allowing for a more accurate fitting of the power consumption of each appliance. To improve the computation efficiency of our model, we present new state constraints, new linear penalty terms, new state transition constraints, and new minimum active time constraints. To extract features from load data containing noise efficiently, we propose automatic feature extraction algorithms based on distributionally robust optimization theory and linear regression. These algorithms can extract power boundary features, power fluctuation features, and minimum active time features. Our proposed method and six state-of-the-art optimization-based methods are tested on the almanac of minutely power dataset (AMPds) and REFIT to verify the performance of the proposed method. The results show that our method outperforms other methods in terms of disaggregation accuracy and computational efficiency. Moreover, our load feature extraction algorithms can effectively reduce the noise interference in the data. Our modeling approach can improve the computational efficiency of other optimization-based methods.