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• W3119481911 abstract "Online non-intrusive load monitoring algorithms have captivated academia and industries as parsimonious solutions for household energy efficiency monitoring as well as a safety control, anomaly detection, and demand-side management. However, the computational energy cost for executing such algorithms should not overcome the promised energy efficiency from the disaggregated appliance specific consumption information feed-backs. Moreover, the energy efficiency of cloud computing systems is also becoming a concern for the environment due to carbon emission. This study analyzes the energy spent to execute NILM algorithms via computation cost estimation and prediction using computing system-level power monitoring and data-driven approaches. A generic framework for an automated algorithm cost monitoring and modeling methodologies is devised for large load scale deployment of Cloud-based Online-NILM algorithms. The efficacy of the proposed approach was examined and validated on two computing system use-cases, i.e., Dedicated Server and Cloud Virtual Server. The prediction models, developed using statistical and machine learning tools, demonstrate the promising applicability of the data-driven approach with a very high prediction accuracy without detailed knowledge of the computing systems and the algorithm." @default.
• W3119481911 created "2021-01-18" @default.
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• W3119481911 date "2022-10-01" @default.
• W3119481911 modified "2023-09-28" @default.
• W3119481911 title "Computational Cost Analysis and Data-Driven Predictive Modeling of Cloud-Based Online-NILM Algorithm" @default.
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• W3119481911 doi "https://doi.org/10.1109/tcc.2021.3051766" @default.
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