An Overview of a New Statistical Non-Intrusive Load Monitoring (NILM) Analysis and Recognition Approach for Domestic Environments: DENARDO

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10256300" target="_blank" >RIV/61989100:27240/24:10256300 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/24:10256300

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10615815" target="_blank" >https://ieeexplore.ieee.org/document/10615815</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/MetroLivEnv60384.2024.10615815" target="_blank" >10.1109/MetroLivEnv60384.2024.10615815</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An Overview of a New Statistical Non-Intrusive Load Monitoring (NILM) Analysis and Recognition Approach for Domestic Environments: DENARDO

Popis výsledku v původním jazyce

IP devices are ubiquitously spread, for both residential and industrial purposes, thanks to the low integration costs and rapid development cycle of all-IP-based 5G+ technologies. As a consequence, the engineering community now considers their automatization and energy scheduling/management as relevant research fields. These topics have a striking relevance also for the development of smart city networks. As a drawback, most ID-device applications produce a large amount of data (high-frequency complexity), requiring supervised machine learning algorithms to be properly analyzed. In this research, we focus on the performance of vehicular mobility and imaging systems, recognizing scenarios (with powered-on devices) in real-time, with the help of a simple convolutional neural network, proving the effectiveness of such an innovative low-cost approach.

Název v anglickém jazyce

An Overview of a New Statistical Non-Intrusive Load Monitoring (NILM) Analysis and Recognition Approach for Domestic Environments: DENARDO

Popis výsledku anglicky

IP devices are ubiquitously spread, for both residential and industrial purposes, thanks to the low integration costs and rapid development cycle of all-IP-based 5G+ technologies. As a consequence, the engineering community now considers their automatization and energy scheduling/management as relevant research fields. These topics have a striking relevance also for the development of smart city networks. As a drawback, most ID-device applications produce a large amount of data (high-frequency complexity), requiring supervised machine learning algorithms to be properly analyzed. In this research, we focus on the performance of vehicular mobility and imaging systems, recognizing scenarios (with powered-on devices) in real-time, with the help of a simple convolutional neural network, proving the effectiveness of such an innovative low-cost approach.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2024 IEEE International Workshop on Metrology for Living Environment, MetroLivEnv 2024 : proceedings

ISBN

979-8-3503-8502-1

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

465-469

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Chania

Datum konání akce

12. 6. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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