Intelligent Energy Source Classifier NILM method in Machine Learning: ALGORITHMIC DATA EXTRACTION - Softcover
Synopsis
ILM method requires a group of sensors attached to each appliance to show information about the appliance load measurement used for functions of appliances load monitoring and classification. Connections of sensors and each appliance through smart plugs provide more accurate measurement than the NILM system. The drawback of the ILM is its cost, which is considered to be very high due to the installation complicity of due to the multiple sensor configurations. In the research, the ILM approach is used as middleware on Atlas and OSGi platforms which was implemented to detect a single appliance solution for the reduction of costs. This method can identify real-time controlling and monitoring of household status by detecting each appliance connected to its smart plug.
"synopsis" may belong to another edition of this title.
Search results for Intelligent Energy Source Classifier NILM method in...
Seller Image
Intelligent Energy Source Classifier NILM method in Machine Learning
Published by
LAP LAMBERT Academic Publishing Aug 2021, 2021
ISBN 10: 6204184229
ISBN 13: 9786204184227
New
Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -ILM method requires a group of sensors attached to each appliance to show information about the appliance load measurement used for functions of appliances load monitoring and classification. Connections of sensors and each appliance through smart plugs provide more accurate measurement than the NILM system. The drawback of the ILM is its cost, which is considered to be very high due to the installation complicity of due to the multiple sensor configurations. In the research, the ILM approach is used as middleware on Atlas and OSGi platforms which was implemented to detect a single appliance solution for the reduction of costs. This method can identify real-time controlling and monitoring of household status by detecting each appliance connected to its smart plug. 60 pp. Englisch. Seller Inventory # 9786204184227
Seller Image
Intelligent Energy Source Classifier NILM method in Machine Learning
Published by
LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6204184229
ISBN 13: 9786204184227
New
Softcover
Seller: moluna, Greven, Germany
Seller rating 5 out of 5 stars
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: UDAY KIRAN ELEMASETTYELEMASETTY UDAY KIRAN completed my Master s with publications of six patents followed by one circuit design in IPO Delhi.Including 11 Research papers and 2 research books.an awardee of the young scientist in the . Seller Inventory # 500660870
Buy New
£ 30.45
£ 42.28 shipping
Ships from Germany to U.S.A.
Ships from Germany to U.S.A.
Quantity: Over 20 available
Seller Image
Intelligent Energy Source Classifier NILM method in Machine Learning
Published by
LAP LAMBERT Academic Publishing Aug 2021, 2021
ISBN 10: 6204184229
ISBN 13: 9786204184227
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -ILM method requires a group of sensors attached to each appliance to show information about the appliance load measurement used for functions of appliances load monitoring and classification. Connections of sensors and each appliance through smart plugs provide more accurate measurement than the NILM system. The drawback of the ILM is its cost, which is considered to be very high due to the installation complicity of due to the multiple sensor configurations. In the research, the ILM approach is used as middleware on Atlas and OSGi platforms which was implemented to detect a single appliance solution for the reduction of costs. This method can identify real-time controlling and monitoring of household status by detecting each appliance connected to its smart plug.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 60 pp. Englisch. Seller Inventory # 9786204184227
Seller Image
Intelligent Energy Source Classifier NILM method in Machine Learning : ALGORITHMIC DATA EXTRACTION
Published by
LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6204184229
ISBN 13: 9786204184227
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - ILM method requires a group of sensors attached to each appliance to show information about the appliance load measurement used for functions of appliances load monitoring and classification. Connections of sensors and each appliance through smart plugs provide more accurate measurement than the NILM system. The drawback of the ILM is its cost, which is considered to be very high due to the installation complicity of due to the multiple sensor configurations. In the research, the ILM approach is used as middleware on Atlas and OSGi platforms which was implemented to detect a single appliance solution for the reduction of costs. This method can identify real-time controlling and monitoring of household status by detecting each appliance connected to its smart plug. Seller Inventory # 9786204184227
Seller Image
Intelligent Energy Source Classifier NILM method in Machine Learning | ALGORITHMIC DATA EXTRACTION
Published by
LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6204184229
ISBN 13: 9786204184227
New
Taschenbuch
Seller: preigu, Osnabrück, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Intelligent Energy Source Classifier NILM method in Machine Learning | ALGORITHMIC DATA EXTRACTION | Elemasetty Uday Kiran (u. a.) | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786204184227 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 120495567