The Balanced Window-Based Load Event Optimal Matching for NILM

Abstract

Load event matching is the key for event-based non-intrusive load monitoring (NILM). It aims to find the load event sequence corresponding to the appliance’s operation cycle from all detected load events. Firstly, a novel combinatorial optimization model for load event matching is established. To reduce the dimension of the estimation matrix in the proposed optimization model, the concept of balanced window (BW) is introduced to indicate a period of load operation within which events can be fully matched. Then an iterative load event optimal matching strategy based on nested BWs is proposed, where most inner BWs are processed firstly. For the event matching in one specific BW, a two-stage optimization algorithm is designed. In the first stage, a set of candidate load event sequences is generated by using the depth first search algorithm with pruning, which reduces the solution space of the combinatorial optimization problem. In the second stage, the optimal load event sequences that result in the optimal fit of the aggregated load power is selected. Comparative experiments on the publicly-accessible REDD datasets have shown that the proposed method achieves better performance against the existing unsupervised NILM methods, and is promising for practical implementation.