Nilmtk

Abstract

Non-intrusive load monitoring, or energy disaggregation, aims to separate household energy consumption data collected from a single point of measurement into appliancelevel consumption data. In recent years, the field has rapidly expanded due to increased interest as national deployments of smart meters have begun in many countries. However, empirically comparing disaggregation algorithms is currently virtually impossible. This is due to the different data sets used, the lack of reference implementations of these algorithms and the variety of accuracy metrics employed. To address this challenge, we present the Non-intrusive Load Monitoring Toolkit (NILMTK); an open source toolkit designed specifically to enable the comparison of energy disaggregation algorithms in a reproducible manner. This work is the first research to compare multiple disaggregation approaches across multiple publicly available data sets. Our toolkit includes parsers for a range of existing data sets, a collection of preprocessing algorithms, a set of statistics for describing data sets, two reference benchmark disaggregation algorithms and a suite of accuracy metrics. We demonstrate the range of reproducible analyses which are made possible by our toolkit, including the analysis of six publicly available data sets and the evaluation of both benchmark disaggregation algorithms across such data sets.

Performance evaluation in non‐intrusive load monitoring: Datasets, metrics, and tools—A review

“…-P Precision (Anderson, Bergés, Ocneanu, Benitez, & Moura, 2012;Batra, Kelly, et al, 2014;Beckel et al, 2014;Berges, 2010) (also called positive predictive value-PPV), is the proportion of relevant instances that were reported as being relevant against all the instances that were reported as relevant.…”

“…The root mean square error (Chris Holmes, 2014;Batra, Kelly, et al, 2014 ;Mayhorn et al, 2016) is the standard deviation of the energy estimation errors. The RMSE reports based on how spread-out these errors are.…”

“…One of such challenges is the many issues related with the replication and generalization of research findings (e.g., the lack of proper test and training data and the absence of a formal agreement on how to report the disaggregation results). This important research challenge only recently became the focus of a group of NILM researchers such as Butner et al (2013), Batra, Kelly, et al (2014), , Makonin and Popowich (2014), Mayhorn, Sullivan, Petersen, Butner, and Johnson (2016), and .…”

Non-intrusive load monitoring under residential solar power influx

“…First, authors have used the NILM Tool-Kit (NILMTK) made available in [51] for the dataset conversion and in data pre-filtering stages. But, due to the limitations in the NILMTK software architecture, reusing the existing NILM algorithms available in NILMTK, under the presence of externally inserted solar power influx was not possible.…”

Appliance Event Detection - A Multivariate, Supervised Classification Approach

“…Other metrics, such as admittance or derivation of current, allow identifying appliance events as well, but seem to be not considered for now. Several event-based approaches have been proposed to retrieve detailed consumption information from aggregated load signals [5,10,17]. Event-based NILM approaches differ in performance, based on the number and types of appliances, the sampling frequency of the acquired data, the quality of the event metrics, and the complexity of utilized disaggregation algorithms.…”