An In Depth Study into Using EMI Signatures for Appliance Identification

Energy conservation is a key factor towards long term energy sustainability. Real-time end user energy feedback, using disaggregated electric load composition, can play a pivotal role in motivating consumers towards energy conservation. Recent works have explored using high frequency conducted electromagnetic interference (EMI) on power lines as a single point sensing parameter for monitoring common home appliances. However, key questions regarding the reliability and feasibility of using EMI signatures for non-intrusive load monitoring over multiple appliances across different sensing paradigms remain unanswered. This work presents some of the key challenges towards using EMI as a unique and time invariant feature for load disaggregation. In-depth empirical evaluations of a large number of appliances in different sensing configurations are carried out, in both laboratory and real world settings. Insights into the effects of external parameters such as line impedance, background noise and appliance coupling on the EMI behavior of an appliance are realized through simulations and measurements. A generic approach for simulating the EMI behavior of an appliance that can then be used to do a detailed analysis of real world phenomenology is presented. The simulation approach is validated with EMI data from a router. Our EMI dataset - High Frequency EMI Dataset (HFED) is also released

Poster Abstract: Bits and Watts: Improving energy disaggregation performance using power line communication modems

Non-intrusive load monitoring (NILM) or energy disaggregation, aims to disaggregate a household's electricity consumption into constituent appliances. More than three decades of work in NILM has resulted in the development of several novel algorithmic approaches. However, despite these advancements, two core challenges still exist: i) disaggregating low power consumption appliances and ii) distinguishing between multiple instances of similar appliances. These challenges are becoming increasingly important due to an increasing number of appliances and increased usage of electronics in homes. Previous approaches have attempted to solve these problems using expensive hardware involving high sampling rates better suited to laboratory settings, or using additional number of sensors, limiting the ease of deployment. In this work, we explore using commercial-off-the-shelf (COTS) power line communication (PLC) modems as an inexpensive and easy to deploy alternative solution to these problems. We use the reduction in bandwidth between two PLC modems, caused due to the change in PLC modulation scheme when different appliances are operated as a signature for an appliance. Since the noise generated in the powerline is dependent both on type and location of an appliance, we believe that our technique based on PLC modems can be a promising addition for solving NILM

Approaches to Non-Intrusive Load Monitoring (NILM) in the Home

When designing and implementing an intelligent energy conservation system for the home, it is essential to have insight into the activities and actions of the occupants. In particular, it is important to understand what appliances are being used and when. In the computational sustainability research community this is known as load disaggregation or Non-Intrusive Load Monitoring (NILM). NILM is a foundational algorithm that can disaggregate a home’s power usage into the individual appliances that are running, identify energy conservation opportunities. This depth report will focus on NILM algorithms, their use and evaluation. We will examine and evaluate the anatomy of NILM, looking at techniques using load monitoring, event detection, feature ex- traction, classification, and accuracy measurement.&nbsp

Non-Intrusive Load Monitoring in the OpenHAB Smart Home Framework

Non-intrusive load monitoring (NILM) on elektrienergia tarbimise jälgimise meetod, milles rakendatakse masinõppe meetodeid, et automaatselt vooluvõrku ühendatud seadmete kogutarbest eraldada üksikute seadmete energiatarve. Käesolev bakalaureusetöö kirjeldab reaalajas töötavat NILM lahendust, mis saab sisendina kasutatavad energiatarbimise andmed targa kodu automatiseerimise keskkonnast openHAB. Kogu energiatarbimise disagregeerimiseks kasutatakse vabavaralist tööriista NILMTK, mis võimaldab kogu süsteemi energiatarbimisest üksikute seadmete tarbimist eraldada. Tuvastatud seadmete hetketarbimised saadetakse tagasi openHABi keskkonda, kus neid võib kasutada koduautomaatikas. Arvutiteaduse instituudi värkvõrgu laboris läbi viidud testide tulemused näitavad, et lahendus suudab täpselt tuvastada stabiilse ja suure energiatarbimisega seadmeid (näiteks soojapuhur), kuid on ebatäpne selliste seadmete eristamisel, mille energiatarbimine kõigub palju (näiteks kohvimasin) või moodustab kogutarbimisest vaid väikse osa (näiteks pirn).Non-intrusive load monitoring (NILM) is an approach to energy monitoring, where machine learning techniques are applied to data from a single energy meter to determine the energy consumption of each appliance connected to the local electric network. This thesis presents a real-time NILM solution for the smart home that relies on the home automation platform openHAB for live power readings. NILMTK – an open-source energy disaggregation toolkit – is used to break down the aggregate live energy consumption data to the appliance level in real time. The disaggregated power data are then sent back to openHAB, where they can be displayed to the user and enable further automation. Tests conducted at the University of Tartu Internet of Things and Smart Solutions laboratory indicate high recognition accuracy for appliances with a steady high energy demand (e.g. a space heater), while lower accuracy scores were reported for appliances with a fluctuating power demand (e.g. a coffee maker) and lowpowered appliances that only make up a small proportion of the total energy consumption (e.g. a light bulb)

An efficient scalable time-frequency method for tracking energy usage of domestic appliances using a two-step classification algorithm

Load identification is the practice of measuring electrical signals in a domestic environment in order to identify which electrical appliances are consuming power. One reason for developing a load identification system is to reduce power consumption by increasing consumers’ awareness of which appliances consume most energy. The thesis outlines the development of a load disaggregation method that measures the aggregate electrical signals of a domestic environment and extracts features to identify each power consuming appliance. A single sensor is deployed at the main incoming power point, to sample the aggregate current signal. The method senses when an appliance switches ON or OFF and uses a two-step classification algorithm to identify which appliance has caused the event. Parameters from the current in the temporal and frequency domains are used as features to de- fine each appliance. These parameters are the steady state current harmonics and the rate of change of the transient signal. Each appliance’s electrical characteristics are distinguishable using these parameters. There are three types of loads that an appliance can fall into, linear nonreactive, linear reactive or nonlinear reactive. It has been found that by identifying the load type first, and then using a second classifier to identify individual appliances within these types, the overall accuracy of the identification algorithm is improved

A Feature-Based Model for the Identification of Electrical Devices in Smart Environments

Smart Homes (SHs) represent the human side of a Smart Grid (SG). Data mining and analysis of energy data of electrical devices in SHs, e.g., for the dynamic load management, is of fundamental importance for the decision-making process of energy management both from the consumer perspective by saving money and also in terms of energy redistribution and reduction of the carbon dioxide emission, by knowing how the energy demand of a building is composed in the SG. Advanced monitoring and control mechanisms are necessary to deal with the identification of appliances. In this paper, a model for their automatic identification is proposed. It is based on a set of 19 features that are extracted by analyzing energy consumption, time usage and location from a set of device profiles. Then, machine learning approaches are employed by experimenting different classifiers based on such model for the identification of appliances and, finally, an analysis on the feature importance is provided

Designing Artificial Neural Networks (ANNs) for Electrical Appliance Classification in Smart Energy Distribution Systems

En este proyecto se abordará el problema de la desagregación del consumo eléctrico a través del diseño de sistemas inteligentes, basados en redes neuronales profundas, que puedan formar parte de sistemas más amplios de gestión y distribución de energía. Durante la definición estará presente la búsqueda de una complejidad computacional adecuada que permita una implementación posterior de bajo costo. En concreto, estos sistemas realizarán el proceso de clasificación a partir de los cambios en la corriente eléctrica provocados por los distintos electrodomésticos. Para la evaluación y comparación de las diferentes propuestas se hará uso de la base de datos BLUED.This project will address the energy consumption disaggregation problem through the design of intelligent systems, based on deep artificial neural networks, which would be part of broader energy management and distribution systems. The search for adequate computational complexity that will allow a subsequent implementation of low cost will be present during algorithm definition. Specifically, these systems will carry out the classification process based on the changes caused by the different appliances in the electric current. For the evaluation and comparison of the different proposals, the BLUED database will be used.Máster Universitario en Ingeniería Industrial (M141