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Bi-level Characterization of Manual-Setup Residential Non-Intrusive Demand Disaggregation using Enhanced Differential Evolution

Renewed interest in formalization of non-intrusiveload monitoring algorithms in recent years has been compounded by inroads into efficient optimization and global search strategies. This work attempts to characterize the manual-setup nonintrusive load disaggregation problem on a household scale using a bi-level strategy composed of an enhanced variant of differential evolution as the optimizing algorithm for a highly adaptive disaggregation in the lower level. Enhanced DifferentialEvolution (EDE) offers efficient convergence compared to itsparent stochastic global search strategy, differential evolution (DE); Coupled with generalized metrics to gauge the selectivity of search for optimal training attributes allows for a highly flexible dissolution of the overall demand profile into its constituent steady-state appliance signatures. Accuracy and efficiency is evaluated forthe proposed strategy.