Construction of Smart Grid Load Forecast Model by Edge Computing
This research aims to minimize the unnecessary resource consumption by intelligent Power Grid Systems (PGSs). Edge Computing (EC) technology is used to forecast PGS load and optimize the PGS load forecasting model. Following a literature review of EC and Internet of Things (IoT)-native edge devices,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/9/3028 |
| _version_ | 1797505104602988544 |
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| author | Xudong Pang Xiangchen Lu Hao Ding Josep M. Guerrero |
| author_facet | Xudong Pang Xiangchen Lu Hao Ding Josep M. Guerrero |
| author_sort | Xudong Pang |
| collection | DOAJ |
| description | This research aims to minimize the unnecessary resource consumption by intelligent Power Grid Systems (PGSs). Edge Computing (EC) technology is used to forecast PGS load and optimize the PGS load forecasting model. Following a literature review of EC and Internet of Things (IoT)-native edge devices, an intelligent PGS-oriented Resource Management Scheme (RMS) and PGS load forecasting model are proposed based on task offloading. Simultaneously, an online delay-aware power Resource Allocation Algorithm (RAA) is developed for EC architecture. Finally, comparing three algorithms corroborate that the system overhead decreases significantly with the model iteration. From the 40th iteration, the system overhead stabilizes. Moreover, given no more than 50 users, the average user delay of the proposed delay-aware power RAA is less than 13 s. The average delay of the proposed algorithm is better than that of the other two algorithms. This research contributes to optimizing intelligent PGS in smart cities and improving power transmission efficiency. |
| first_indexed | 2024-03-10T04:13:51Z |
| format | Article |
| id | doaj.art-2d74a2eb8bb14b3a8313f642c04e453f |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T04:13:51Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-2d74a2eb8bb14b3a8313f642c04e453f2023-11-23T08:05:31ZengMDPI AGEnergies1996-10732022-04-01159302810.3390/en15093028Construction of Smart Grid Load Forecast Model by Edge ComputingXudong Pang0Xiangchen Lu1Hao Ding2Josep M. Guerrero3Electrical Engineering Department, Yanshan University, Qinhuangdao 066000, ChinaSchool of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaElectrical Engineering Department, Yanshan University, Qinhuangdao 066000, ChinaDepartment of Energy Technology, Aalborg University, 9220 Aalborg, DenmarkThis research aims to minimize the unnecessary resource consumption by intelligent Power Grid Systems (PGSs). Edge Computing (EC) technology is used to forecast PGS load and optimize the PGS load forecasting model. Following a literature review of EC and Internet of Things (IoT)-native edge devices, an intelligent PGS-oriented Resource Management Scheme (RMS) and PGS load forecasting model are proposed based on task offloading. Simultaneously, an online delay-aware power Resource Allocation Algorithm (RAA) is developed for EC architecture. Finally, comparing three algorithms corroborate that the system overhead decreases significantly with the model iteration. From the 40th iteration, the system overhead stabilizes. Moreover, given no more than 50 users, the average user delay of the proposed delay-aware power RAA is less than 13 s. The average delay of the proposed algorithm is better than that of the other two algorithms. This research contributes to optimizing intelligent PGS in smart cities and improving power transmission efficiency.https://www.mdpi.com/1996-1073/15/9/3028edge computingintelligent Power Grid System (PGS)PGS loadresource management |
| spellingShingle | Xudong Pang Xiangchen Lu Hao Ding Josep M. Guerrero Construction of Smart Grid Load Forecast Model by Edge Computing Energies edge computing intelligent Power Grid System (PGS) PGS load resource management |
| title | Construction of Smart Grid Load Forecast Model by Edge Computing |
| title_full | Construction of Smart Grid Load Forecast Model by Edge Computing |
| title_fullStr | Construction of Smart Grid Load Forecast Model by Edge Computing |
| title_full_unstemmed | Construction of Smart Grid Load Forecast Model by Edge Computing |
| title_short | Construction of Smart Grid Load Forecast Model by Edge Computing |
| title_sort | construction of smart grid load forecast model by edge computing |
| topic | edge computing intelligent Power Grid System (PGS) PGS load resource management |
| url | https://www.mdpi.com/1996-1073/15/9/3028 |
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