Household Energy Disaggregation Based on Pattern Consumption Similarities

Abstract

Non-intrusive load monitoring allows breaking down the aggregated household consumption into a detailed consumption per appliance, without installing extra hardware, apart of a smart meter. Breakdown information is very useful for both users and electric companies, to provide an accurate characterization of energy consumption, avoid peaks, and elaborate special tariffs to reduce the cost of the electricity bill. This article presents an approach for energy consumption disaggregation in residential households, based on detecting similar patterns of recorded consumption from labeled datasets. The proposed algorithm is evaluated using four different instances of the problem, which use synthetically generated data based on real energy consumption. Each generated dataset normalize the consumption values of the appliances to create complex scenarios. The nilmtk framework is used to process the results and to perform a comparison with two built-in algorithms provided by the framework, based on combinatorial optimization and factorial hidden Markov model. The proposed algorithm was able to achieve accurate results, despite the presence of ambiguity between the consumption of different appliances or the difference of consumption between training appliances and test appliances.