Smart City Gnosys

Smart city article details

Title	A Method For Local Analysis Of Electric Energy Monitoring Data
ID_Doc	2566
Authors	Wu H.; Wang C.; Zhou Y.; Xie W.; Chen C.
Year	2022
Published	International Journal of Innovative Computing, Information and Control, 18, 1
DOI	http://dx.doi.org/10.24507/ijicic.18.01.105
Abstract	The development of Internet of Things (IoT) technologies has provided potential for real-time monitoring and control of environment in smart cities. In the field of energy management, energy prediction can be carried out by sensing and analyzing dynamic environmental information of the energy consumption side, which provides decision support for energy production to avoid excess or insufficient energy supply and achieve agile production. However, due to the complexity and diversity of the IoT data, it is difficult to build an efficient energy prediction system that reflects the dynamics of the IoT environment. To address this problem, a short-term energy prediction system based on edge computing architecture is proposed, in which data acquisition, data processing and regression prediction are distributed in sensing nodes, routing nodes and central server respectively. Semantics and stream processing techniques are utilized to support efficient IoT data acquisition and processing. In addition, an online multi-branch network model adapted to the characteristics of IoT data is implemented for energy prediction. A real-world case study of energy prediction in a regional energy system is given to verify the feasibility and efficiency of our system. The results show that the system can provide support for real-time energy prediction with high precision in a promising way. © 2022, ICIC International. All rights reserved.
Author Keywords	Edge computing; Internet of Things; Multibranch network; Short-term energy prediction; Stream processing

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