Electric appliance classification based on distributed high resolution current sensing

Abstract

Today's solutions to inform residents about their electricity consumption are mostly confined to displaying aggregate readings collected at meter level. A reliable identification of appliances that require disproportionate amounts of energy for their operation is generally unsupported by these systems, or at least requires significant manual configuration efforts. We address this challenge by placing low-cost measurement and actuation units into the mains connection of appliances. The distributed sensors capture the current flow of individual appliances at a sampling rate of 1.6kHz and apply local signal processing to the readings in order to extract characteristic fingerprints. These fingerprints are communicated wirelessly to the evaluation server, thus keeping the required airtime and energy demand of the transmission low. The evaluation server employs machine learning techniques and caters for the actual classification of attached electric appliances based on their fingerprints, enabling the correlation of consumption data and the appliance identity. Our evaluation is based on more than 3,000 current consumption fingerprints, which we have captured for a range of household appliances. The results indicate that a high accuracy is achieved when locally extracted current consumption fingerprints are used to classify appliances.

Context-Based Energy Disaggregation in Smart Homes

“…In addition, Shahriar et al [36] proposed a similar approach which uses temporal and sensing information but with the aim of performing an appliance classification of power traces of single or a combination of two devices. Furthermore, a private dataset has been used in both [35] and [36], thus non-comparable results have been produced; conversely, our work uses a public dataset [10], which is thus available also to other researchers. Several open data sets are available at this time: high frequency datasets such as BLUED (Building-Level fUlly-labeled dataset for Electricity Disaggregation) [37] or REDD (Reference Energy Disaggregation Dataset) [38]; low frequency data sets such as TRACEBASE [10] or ultra-low frequency as AMPds (Almanac of Minutely Power dataset) [39].…”

“…Moreover, the proposed approach has been tested using data gathered from real home environments and made available as an open dataset by the Technische Universität Darmstadt (i.e., Tracebase [10]). In our opinion, this choice may be scientifically relevant since it eases the comparison of results with future work and encourages further improvements.…”

Appliance Recognition in an OSGi-Based Home Energy Management Gateway

“…These distributed sensing platforms support the fine-grained monitoring and control (e.g., switch on/off) of the connected devices but do not offer capabilities for identifying the type of attached device [7].…”

Automatic identification of electrical appliances using smart plugs

“…Various identification algorithms have been used to analyze the consumptions of electrical devices: K-Nearest Neighbors, Decision Trees, Naïve Bayes, Bayesian Networks, Gaussian Mixture Models [1,7,11,12]. Modelling techniques able to handle the temporal and state-based nature of some signals have also been proposed such as Hidden Markov Models and Factorial Hidden Markov Models (FHMMs) [3,5,10,13].…”