887 research outputs found
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
A Scoping Review of Energy Load Disaggregation
Energy load disaggregation can contribute to balancing power grids by
enhancing the effectiveness of demand-side management and promoting
electricity-saving behavior through increased consumer awareness. However, the
field currently lacks a comprehensive overview. To address this gap, this paper
con-ducts a scoping review of load disaggregation domains, data types, and
methods, by assessing 72 full-text journal articles. The findings reveal that
domestic electricity consumption is the most researched area, while others,
such as industrial load disaggregation, are rarely discussed. The majority of
research uses relatively low-frequency data, sampled between 1 and 60 seconds.
A wide variety of methods are used, and artificial neural networks are the most
common, followed by optimization strategies, Hidden Markov Models, and Graph
Signal Processing approaches
Integration of Legacy Appliances into Home Energy Management Systems
The progressive installation of renewable energy sources requires the
coordination of energy consuming devices. At consumer level, this coordination
can be done by a home energy management system (HEMS). Interoperability issues
need to be solved among smart appliances as well as between smart and
non-smart, i.e., legacy devices. We expect current standardization efforts to
soon provide technologies to design smart appliances in order to cope with the
current interoperability issues. Nevertheless, common electrical devices affect
energy consumption significantly and therefore deserve consideration within
energy management applications. This paper discusses the integration of smart
and legacy devices into a generic system architecture and, subsequently,
elaborates the requirements and components which are necessary to realize such
an architecture including an application of load detection for the
identification of running loads and their integration into existing HEM
systems. We assess the feasibility of such an approach with a case study based
on a measurement campaign on real households. We show how the information of
detected appliances can be extracted in order to create device profiles
allowing for their integration and management within a HEMS
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)
Efficiency and Optimization of Buildings Energy Consumption: Volume II
This reprint, as a continuation of a previous Special Issue entitled “Efficiency and Optimization of Buildings Energy Consumption”, gives an up-to-date overview of new technologies based on Machine Learning (ML) and Internet of Things (IoT) procedures to improve the mathematical approach of algorithms that allow control systems to be improved with the aim of reducing housing sector energy consumption
Real-Time Recognition Non-Intrusive Electrical Appliance Monitoring Algorithm for a Residential Building Energy Management System
The concern of energy price hikes and the impact of climate change because of energy generation and usage forms the basis for residential building energy conservation. Existing energy meters do not provide much information about the energy usage of the individual appliance apart from its power rating. The detection of the appliance energy usage will not only help in energy conservation, but also facilitate the demand response (DR) market participation as well as being one way of building energy conservation. However, energy usage by individual appliance is quite difficult to estimate. This paper proposes a novel approach: an unsupervised disaggregation method, which is a variant of the hidden Markov model (HMM), to detect an appliance and its operation state based on practicable measurable parameters from the household energy meter. Performing experiments in a practical environment validates our proposed method. Our results show that our model can provide appliance detection and power usage information in a non-intrusive manner, which is ideal for enabling power conservation efforts and participation in the demand response market.1176Ysciescopu
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