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Energy Disaggregation Methods for Commercial Buildingsusing Smart Meter and Operational data
NEC Laboratories Europe, Heidelberg.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
Number of Authors: 2
2017 (English)In: The Workshops of the Thirty-First AAAI Conference on Artificial Intelligence: Artificial Intelligence for Smart Grids and Smart Buildings, 2017Conference paper, (Refereed)
Abstract [en]

One of the key information pieces in improving energy efficiency of buildings is the appliance level breakdown of energy consumption. Energy disaggregation is the process of obtaining this breakdown from a building level aggregate data using computational techniques. Most of the current research focuses on residential buildings, obtaining this information from a single smart meter and often relying on high frequency data. This work is directed at commercial buildings equipped with building management and automation systems providing low frequency operational and contextual data. This paper presents a machine learning method to disaggregate energy consumption of the building using this operational data as input features. Experimental results on two publicly available datasets demonstrate the effectiveness of the approach, which surpasses existing methods. For all but one appliance of House 2 of the publicly available REDD dataset, improvements in normalized error in assigned power range between 20% (Lighting) and 220% (Stove). For another dataset from an educational facility in Singapore, disaggregation accuracy of 92% is reported for the facility’s cooling system.

Place, publisher, year, edition, pages
2017.
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-61569OAI: oai:DiVA.org:ltu-61569DiVA: diva2:1067424
Conference
31st AAAI Conference on Artificial Intelligence (AAAI-17, San Francisco, 4–9 February 2017
Available from: 2017-01-20 Created: 2017-01-20 Last updated: 2017-02-22Bibliographically approved

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  • apa
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