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Algorithmic Detection of Home Appliances from Smart Meter Data: Proof-of-concept implementation of Energy Disaggregation algorithms based on data generated from smart meters - Softcover
Synopsis
Reducing the overall energy waste is one of the most pressing challenges of mankind. The energy consumption of individuals can be reduced by providing them with information about the consumption of single appliances in their household. The field of Non-intrusive Appliance Load Monitoring or Energy Disaggregation detects single devices from aggregated loads. Smart meters provide an easy solution to extract momentary values of the device-aggregated energy consumption for further processing. This publication summarizes a proof-of-concept implementation from data extraction via standard smart meters to the detection of appliances of interest (AOIs). Data extraction is based on a low cost hardware with an extraction computer script. The developed disaggregation algorithms were trained with device parameters to detect three AOIs: freezer, dishwasher, and dryer. Through the generality of the concept, the algorithms could be trained to detect other appliance models or classes. Leveraging standard interfaces, the implementation could be reproduced in different households with an installed standard smart meter.
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About the Author
Sebastian Schaal holds a B.Sc. degree in Electrical Engineering & Information Technology from the Technische Universität München and completed the Honours Degree program from the Center of Digital Technology & Management. This publication was supervised by both institutions and resulted from a product development project on smart energy solutions.
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Algorithmic Detection of Home Appliances from Smart Meter Data: Proof-of-concept implementation of Energy Disaggregation algorithms based on data generated from smart meters
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Algorithmic Detection of Home Appliances from Smart Meter Data
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AV Akademikerverlag Aug 2015, 2015
ISBN 10: 3639858468
ISBN 13: 9783639858464
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Reducing the overall energy waste is one of the most pressing challenges of mankind. The energy consumption of individuals can be reduced by providing them with information about the consumption of single appliances in their household. The field of Non-intrusive Appliance Load Monitoring or Energy Disaggregation detects single devices from aggregated loads. Smart meters provide an easy solution to extract momentary values of the device-aggregated energy consumption for further processing. This publication summarizes a proof-of-concept implementation from data extraction via standard smart meters to the detection of appliances of interest (AOIs). Data extraction is based on a low cost hardware with an extraction computer script. The developed disaggregation algorithms were trained with device parameters to detect three AOIs: freezer, dishwasher, and dryer. Through the generality of the concept, the algorithms could be trained to detect other appliance models or classes. Leveraging standard interfaces, the implementation could be reproduced in different households with an installed standard smart meter. 60 pp. Englisch. Seller Inventory # 9783639858464
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Algorithmic Detection of Home Appliances from Smart Meter Data : Proof-of-concept implementation of Energy Disaggregation algorithms based on data generated from smart meters
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Reducing the overall energy waste is one of the most pressing challenges of mankind. The energy consumption of individuals can be reduced by providing them with information about the consumption of single appliances in their household. The field of Non-intrusive Appliance Load Monitoring or Energy Disaggregation detects single devices from aggregated loads. Smart meters provide an easy solution to extract momentary values of the device-aggregated energy consumption for further processing. This publication summarizes a proof-of-concept implementation from data extraction via standard smart meters to the detection of appliances of interest (AOIs). Data extraction is based on a low cost hardware with an extraction computer script. The developed disaggregation algorithms were trained with device parameters to detect three AOIs: freezer, dishwasher, and dryer. Through the generality of the concept, the algorithms could be trained to detect other appliance models or classes. Leveraging standard interfaces, the implementation could be reproduced in different households with an installed standard smart meter. Seller Inventory # 9783639858464
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Algorithmic Detection of Home Appliances from Smart Meter Data
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Schaal SebastianSebastian Schaal holds a B.Sc. degree in Electrical Engineering & Information Technology from the Technische Universitaet Muenchen and completed the Honours Degree program from the Center of Digital Technology & Managem. Seller Inventory # 158074886
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Algorithmic Detection of Home Appliances from Smart Meter Data
Published by
AV Akademikerverlag Aug 2015, 2015
ISBN 10: 3639858468
ISBN 13: 9783639858464
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Taschenbuch. Condition: Neu. Neuware -Reducing the overall energy waste is one of the most pressing challenges of mankind. The energy consumption of individuals can be reduced by providing them with information about the consumption of single appliances in their household. The field of Non-intrusive Appliance Load Monitoring or Energy Disaggregation detects single devices from aggregated loads. Smart meters provide an easy solution to extract momentary values of the device-aggregated energy consumption for further processing. This publication summarizes a proof-of-concept implementation from data extraction via standard smart meters to the detection of appliances of interest (AOIs). Data extraction is based on a low cost hardware with an extraction computer script. The developed disaggregation algorithms were trained with device parameters to detect three AOIs: freezer, dishwasher, and dryer. Through the generality of the concept, the algorithms could be trained to detect other appliance models or classes. Leveraging standard interfaces, the implementation could be reproduced in different households with an installed standard smart meter.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 60 pp. Englisch. Seller Inventory # 9783639858464
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