Improving Nonintrusive Load Monitoring Efficiency via a Hybrid Programing Method

Nonintrusive load monitoring (NILM) aims to disaggregate the total power consumption profile measured at the household power inlet into device-level insights. While many studies focus on the modeling methodologies, few of them address the challenge of the computation efficiency which is critical for practical applications. The NILM problem is essentially a nondeterministic polynomial-time hard problem, meaning that obtaining the exact optimal solution is technically intractable. This paper proposes a fast method to address the approximation to the solutions of such problems from an optimization point of view. It is shown that by taking advantage of the constraint programing framework, the computational efficiency of the proposed NILM scheme can be significantly improved while comparable solution accuracy can also be preserved. Simulations conducted on the popular public datasets validate the effectiveness and efficiency of our proposed method.