A mixed-integer programming approach for industrial non-intrusive load monitoring

With the development of the smart grid, more load data can be collected and utilized to facilitate bidirectional communications between the supply-side and demand-side. More detailed data can achieve better results in applications, but it is not feasible due to sensor placement and data transmission limitations. Non-intrusive load monitoring (NILM) is an exceptionally low-cost solution for providing appliance-level load information of a building without installing extra sensors. While most current studies focus on NILM in residential buildings, the potential benefit of industrial NILM is preferable due to the grander power consumption scale and more professional management. This paper considers the challenges and strengths of industrial NILM, and a mixed-integer programming NILM approach is proposed for flowline industries. This approach separately models equipment with different load features, resulting in better performance in industrial scenarios containing various loads. Temporal dependencies between equipment are exploited by recognizing the statistical regularity of load fluctuation and are introduced as a novel flowline constraint in the model. A pulse width constraint that restricts the state duration of equipment is also introduced to improve performance. Several cases are designed and tested on two public industrial datasets to evaluate the effectiveness and transferability of the model. The classical factorial hidden Markov model (FHMM), a state-of-the-art matrix factorization method, and a deep learning model are used as benchmarks for comparison.