Energy Disaggregation Considering Least Square Error And Temporal Sparsity: A Multi-Objective Evolutionary Approach

In energy disaggregation (ED), an accurate estimation of each appliance power consumption over time is crucial for practical applications. Most optimization-based ED approaches are single-objective and the disaggregated appliance power consumption profiles provided by them do not match the actual operational characteristics of the devices. In this paper, we propose a Multi-objective ED (MOED) framework considering two conflicting objectives 1) minimizing the least square error between the measured and estimated aggregated signals; and 2) minimizing the temporal sparsity of appliance switching events. The multi-objective consideration results in a set of trade-off solutions where each solution represents a prospective disaggregation corresponding to the operation of different devices present. From the trade-off set, a disaggregated solution where the operational characteristics of appliances are close to practical operation is identified by evaluating statistical similarity. For each prospective solution, statistical similarity is evaluated with respect to a set of reference signals corresponding to the practical device operation provided by the device manufacturer. In addition, to effectively solve the MOED problem, an initialization based on Integer Programming (IP), problem-specific mutation and crossover operators are proposed. The performance of the proposed MOED framework is tested on real-world dataset from University of Victoria (UVic) and compared with other optimization approaches. On various indicators MOED shows better performance than conventional and improved Integer Programming based single-objective ED algorithms. In addition, the disaggregated signals provided by MOED are close to the practical operational characteristics of the respective appliances compared to disaggregated signals provided by the state-of-the-art ED approaches.