Designing Collaborative Edge Computing for Electricity Heterogeneous Data Based on Social IoT Systems

Yong Cheng, Jie Du, Yonggang Yang, Zhibao Ma, Ning Li, Jia Zhao and Di Wu
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Yong Cheng: State Grid Shaanxi Electric Power Co. Ltd., China
Jie Du: State Grid Shaanxi Electric Power Co. Ltd., China
Yonggang Yang: State Grid Shaanxi Electric Power Co. Ltd., China
Zhibao Ma: State Grid Shaanxi Marketing Service Center, China
Ning Li: State Grid Shaanxi Electric Power Co. Ltd., China
Jia Zhao: China United Network Communications Group Corporation Limited, China
Di Wu: State Grid Shaanxi Marketing Service Center, China

International Journal of Distributed Systems and Technologies (IJDST), 2022, vol. 13, issue 7, 1-22

Abstract: Power generation, transmission, maintenance costs, and electricity prices are heavily influenced by accurate load forecasts at energy suppliers' operation centers. Every aspect of our life has been transformed by the social internet of things (SIoT). Collaborative edge computing (CEC) has emerged as a new paradigm for meeting the demands of the internet of things by alleviating resource congestion (IoT). Remote devices can connect to CEC's processing, storage, and network resources. About short-term electrical load forecasting, this study explores the application of feed-forward deep neurological networking (FF-DNN) and recurrent deep neuronal networking (R-DNN) methods and analyzes their accuracy and computing performance. A dynamic prediction system using a deep neural network (DPS-DNN) is proposed in this research. The recently unveiled smartgrid with the results shows the higher performance of the proposed DPS-DNN model than the existing models with an enhancement of 93.15% based on collaborative edge networks based on SIoT.

Date: 2022
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