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Smart city article details

Title	Cluster-Based Energy Load Profiling On Residential Smart Buildings
ID_Doc	14511
Authors	Savvopoulos A.; Kalogeras G.; Anagnostopoulos C.; Alexakos C.; Sioutas S.; Kalogeras A.P.
Year	2020
Published	IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 2020-September
DOI	http://dx.doi.org/10.1109/ETFA46521.2020.9212161
Abstract	Percentage of population living in cities is expected to reach 60% by 2030, accounting for 60% - 80% of world annual energy needs and making the impact of energy efficient solutions in cities quite significant for environmental protection and fighting climate change. The building sector uses about 40% of European energy and emits approximately 1/3 of greenhouse gas emissions. Black-box measurement based modeling methods allow the estimation of consumption in buildings relying on smart metering devices installed. Vast amount of data generated poses new challenges with reference to their handling and timely processing. The paper presents an approach related to building energy load profiling utilising profile compression and clustering. It discusses the application of different clustering algorithms through their experimental evaluation. © 2020 IEEE.
Author Keywords	Load Energy Profiling; Smart Building; Smart Cities; Smart Energy

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