9,714 research outputs found
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
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
Load Hiding of Household's Power Demand
With the development and introduction of smart metering, the energy
information for costumers will change from infrequent manual meter readings to
fine-grained energy consumption data. On the one hand these fine-grained
measurements will lead to an improvement in costumers' energy habits, but on
the other hand the fined-grained data produces information about a household
and also households' inhabitants, which are the basis for many future privacy
issues. To ensure household privacy and smart meter information owned by the
household inhabitants, load hiding techniques were introduced to obfuscate the
load demand visible at the household energy meter. In this work, a
state-of-the-art battery-based load hiding (BLH) technique, which uses a
controllable battery to disguise the power consumption and a novel load hiding
technique called load-based load hiding (LLH) are presented. An LLH system uses
an controllable household appliance to obfuscate the household's power demand.
We evaluate and compare both load hiding techniques on real household data and
show that both techniques can strengthen household privacy but only LLH can
increase appliance level privacy
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The diversity of residential electricity demand – a comparative analysis of metered and simulated data
A comparative study between simulated residential electricity demand data and metered data from theUK Household Electricity Survey is presented. For this study, a high-resolution probabilistic model wasused to test whether this increasingly widely used modelling approach provides an adequate represen-tation of the statistical characteristics the most comprehensive dataset of metered electricity demandavailable in the UK. Both the empirical and simulated electricity consumption data have been analysedon an aggregated level, paying special attention to the mean daily load profiles, the distribution of house-holds with respect to the total annual demands, and the distributions of the annual demands of particularappliances. A thorough comparison making use of both qualitative and quantitative methods was madebetween simulated datasets and it’s metered counterparts. Significant discrepancies were found in thedistribution of households with respect to both overall electricity consumption and consumption ofindividual appliances. Parametric estimates of the distributions of metered data were obtained, and theanalytic expressions for both the density function and cumulative distribution are given. These can beincorporated into new and existent modelling frameworks, as well as used as tools for further analysis
GREEND: An Energy Consumption Dataset of Households in Italy and Austria
Home energy management systems can be used to monitor and optimize
consumption and local production from renewable energy. To assess solutions
before their deployment, researchers and designers of those systems demand for
energy consumption datasets. In this paper, we present the GREEND dataset,
containing detailed power usage information obtained through a measurement
campaign in households in Austria and Italy. We provide a description of
consumption scenarios and discuss design choices for the sensing
infrastructure. Finally, we benchmark the dataset with state-of-the-art
techniques in load disaggregation, occupancy detection and appliance usage
mining
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