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Cited by 56 publications
(22 citation statements)
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“…Further, technologies (e.g., Rancher [18], k3s [19]) have been developed for management of services in cloud and edge computing environments. Finally, energy consumption prediction in power plants has been studied earlier [20,21]. However, architecture design in cloud-edge continuum, focusing on the integration between service management technologies for building energy modelling, has not been studied according to the authors' best knowledge.…”
Section: Memorymentioning
confidence: 99%
See 1 more Smart Citation
“…Further, technologies (e.g., Rancher [18], k3s [19]) have been developed for management of services in cloud and edge computing environments. Finally, energy consumption prediction in power plants has been studied earlier [20,21]. However, architecture design in cloud-edge continuum, focusing on the integration between service management technologies for building energy modelling, has not been studied according to the authors' best knowledge.…”
Section: Memorymentioning
confidence: 99%
“…Edge gateways were used for data collection and training of the model, and cloud was used as a secondary storage, and for improving prediction latency (offloading). Another smart city-related system applied edge-nodes for data collection, and a DNN was trained continuously for optimising energy consumption in power plants [20]. An ML platform has been proposed for optimising energy consumption in power plants with minimal interruption to the prediction service [21].…”
Section: Related Workmentioning
confidence: 99%
“…5) Edge computing: Most of the customers use latencysensitive applications and, in no time, are always ready to shift to other providers if this latency increases. And edge computing is a technology that helps to reduce this latency by considering geographical locations and some laws of physics [20]. Moreover, most of the organizations are moving to industry 4.0, which supports digitization, edge computing helps in attaining better digital quality with increasing demands.…”
Section: A Trends Impacting I Andomentioning
confidence: 99%
“…It is verified that the model can achieve energyefficient clustering, peak shaving, optimal scheduling, and load balancing optimization. Luo et al [37] proposed a short-term energy prediction system based on edge computing architecture. e system distributes data acquisition, data processing, and regression prediction on sensor nodes, routing nodes, and central servers, respectively.…”
Section: To Improve Energy Efficiency Through a Series Of Technological Innovationmentioning
confidence: 99%