NILM techniques for intelligent home energy management and ambient assisted living: a review

The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.Agência financiadora: Programa Operacional Portugal 2020 and Programa Operacional Regional do Algarve 01/SAICT/2018/39578 Fundação para a Ciência e Tecnologia through IDMEC, under LAETA: SFRH/BSAB/142998/2018 SFRH/BSAB/142997/2018 UID/EMS/50022/2019 Junta de Comunidades de Castilla-La-Mancha, Spain: SBPLY/17/180501/000392 Spanish Ministry of Economy, Industry and Competitiveness (SOC-PLC project): TEC2015-64835-C3-2-R MINECO/FEDERinfo:eu-repo/semantics/publishedVersio

NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring

Non-intrusive load monitoring, or energy disaggregation, aims to separate household energy consumption data collected from a single point of measurement into appliance-level 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.Comment: To appear in the fifth International Conference on Future Energy Systems (ACM e-Energy), Cambridge, UK. 201

Latent Bayesian melding for integrating individual and population models

In many statistical problems, a more coarse-grained model may be suitable for population-level behaviour, whereas a more detailed model is appropriate for accurate modelling of individual behaviour. This raises the question of how to integrate both types of models. Methods such as posterior regularization follow the idea of generalized moment matching, in that they allow matching expectations between two models, but sometimes both models are most conveniently expressed as latent variable models. We propose latent Bayesian melding, which is motivated by averaging the distributions over populations statistics of both the individual-level and the population-level models under a logarithmic opinion pool framework. ln a case study on electricity disaggregation, which is a type of single channel blind source separation problem, we show that latent Bayesian melding leads to significantly more accurate predictions than an approach based solely on generalized moment matching

Energy Disaggregation using Two-Stage Fusion of Binary Device Detectors

A data-driven methodology to improve the energy disaggregation accuracy during Non-Intrusive Load Monitoring is proposed. In detail, the method is using a two-stage classification scheme, with the first stage consisting of classification models processing the aggregated signal in parallel and each of them producing a binary device detection score, and the second stage consisting of fusion regression models for estimating the power consumption for each of the electrical appliances. The accuracy of the proposed approach was tested on three datasets (ECO, REDD and iAWE), which are available online, using four different classifiers. The presented approach improves the estimation accuracy by up to 4.1% with respect to a basic energy disaggregation architecture, while the improvement on device level was up to 10.1%. Analysis on device level showed significant improvement of power consumption estimation accuracy especially for continuous and non-linear appliances across all evaluated datasets

Filtering in non-Intrusive load monitoring

Being able to track appliances energy usage without the need of sensors can help occupants reduce their energy consumption. Non-intrusive load monitoring (NILM) is one name for this topic. One of the hardest problems NILM faces is the ability to run unsupervised – discovering appliances without prior knowledge – and to run independent of the differences in appliance mixes and operational characteristics found in various countries and regions. This thesis showcases two filters that are used to denoise power signals, which results in better clustering accuracy for NILM event based methods. Both filters show to outperform a state-of-the-art denoising filter, in terms of run-time. A fully unsupervised NILM solution is presented, the algorithm is based on a hybrid knapsack problem with a Gaussian mixture model. Finally, a novel metric is developed to measure NILM disaggregation performance. The metric shows to be robust under a set of fundamental test cases