An Efficient and Accurate Nonintrusive Load Monitoring Scheme for Power Consumption

Nonintrusive load monitoring (NILM) has attracted tremendous attention owing to its cost efficiency in electricity and sustainable development. NILM aims at acquiring individual appliance power consumption rates using an aggregated power smart meter reading. Each individual appliance's power consumption enables users to monitor their electricity usage habits for rational saving strategies. This is also a valuable tool for detecting failure in appliances. However, the major barriers facing NILM schemes are issues of accurately capturing the features of each appliance and decreasing the computing time. Motivated by these challenges, we propose a new, efficient, and accurate NILM scheme, consisting of a learning step and a decomposing step. In the learning step, we propose the fast search-and-find of density peaks (FSFDPs) clustering algorithm aimed at capturing the features of the power consumption patterns of appliances. In the decomposing step, we propose a genetic algorithm (GA)-based matching algorithm to estimate the power consumption of each individual appliance using the aggregated power reading. Using elitist and catastrophic strategies, this step reduces the searching space to achieve considerable efficiency. Experimental results using the reference energy disaggregation dataset (REDD) indicate that our proposed scheme promotes accuracy by 10% and reduces the decomposing time by half.