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• W2979455747 endingPage "4443" @default.
• W2979455747 startingPage "4443" @default.
• W2979455747 abstract "Electrical energy management, or demand-side management (DSM), in a smart grid is very important for electrical energy savings. With the high penetration rate of the Internet of Things (IoT) paradigm in modern society, IoT-oriented electrical energy management systems (EMSs) in DSM are capable of skillfully monitoring the energy consumption of electrical appliances. While many of today’s IoT devices used in EMSs take advantage of cloud analytics, IoT manufacturers and application developers are devoting themselves to novel IoT devices developed at the edge of the Internet. In this study, a smart autonomous time and frequency analysis current sensor-based power meter prototype, a novel IoT end device, in an edge analytics-based artificial intelligence (AI) across IoT (AIoT) architecture launched with cloud analytics is developed. The prototype has assembled hardware and software to be developed over fog-cloud analytics for DSM in a smart grid. Advanced AI well trained offline in cloud analytics is autonomously and automatically deployed onsite on the prototype as edge analytics at the edge of the Internet for online load identification in DSM. In this study, auto-labeling, or online load identification, of electrical appliances monitored by the developed prototype in the launched edge analytics-based AIoT architecture is experimentally demonstrated. As the proof-of-concept demonstration of the prototype shows, the methodology in this study is feasible and workable." @default.
• W2979455747 created "2019-10-18" @default.
• W2979455747 creator A5006886162 @default.
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• W2979455747 date "2019-10-14" @default.
• W2979455747 modified "2023-09-26" @default.
• W2979455747 title "A Smart Autonomous Time- and Frequency-Domain Analysis Current Sensor-Based Power Meter Prototype Developed over Fog-Cloud Analytics for Demand-Side Management" @default.
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• W2979455747 doi "https://doi.org/10.3390/s19204443" @default.
• W2979455747 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6832205" @default.
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• W2979455747 hasPublicationYear "2019" @default.
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