Household Appliance Non-Intrusive Load Monitoring Using Alternating Direction Method of Multipliers Based on Relaxation Distance and Neighborhood Search

Non-intrusive load monitoring (NILM), a sophisticated load monitoring technology, has garnered considerable interest for its potential to assist consumers in lowering their energy expenditures. In this paper, we present a continuous non-convex optimization model for NILM that employs the norm-box constraint to convert the discrete integer variables in the model into continuous ones. Subsequently, we apply the alternating direction method of multipliers (ADMM) algorithm to tackle the non-convex problem. To enhance the sluggish convergence of the ADMM algorithm, we introduce a linear penalty term based on relaxation distance (RD) to supplant the conventional quadratic penalty term. Furthermore, we devise a heuristic refinement method based on neighborhood search (NS) to augment the solution quality of our algorithm. Simultaneously, by utilizing a dynamic window partitioning technique, the NILM task can be split into multiple small subtasks. These subtasks can be allocated to multiple consumer electronics with computing capabilities to achieve distributed computing. Ultimately, we validate our proposed algorithm on the AMPds dataset, and the experimental results demonstrate that it has faster convergence and yields better solutions compared to a state-of-the-art solver and traditional ADMM algorithms. Using our algorithm, the NILM system can offer consumers efficient, convenient, and economical services.