A novel cloud-edge collaboration based short-term load forecasting method for smart grid

被引:4
作者
Wang, Ai-Xia [1 ]
Li, Jing-Jiao [1 ]
机构
[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang, Peoples R China
关键词
smart grid; short-term load forecasting; edge computing; cloud-edge collaboration; reinforcement learning; MANAGEMENT;
D O I
10.3389/fenrg.2022.977026
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
With the increasing development of smart grid technology, short-term load forecasting becomes particularly important in power system operation. However, the design of accurate and reliable short-term load forecasting methods and models is challenging due to the volatility and intermittency of renewable energy sources, as well as the privacy and individual characteristics of electricity consumption data from user data. To overcome this issue, in this paper, a novel cloud-edge collaboration short-term load forecasting method is proposed for smart grid. In order to reduce the computational load of edge nodes and improve the accuracy of node prediction, we use the method of building a model pre-training pool to train multiple pre-training models in the cloud layer at the same time. Then we use edge nodes to retrain the pre-trained model, select the optimal model and update the model parameters to achieve short-term load forecasting. To assure the validity of the model and the confidentiality of private data, we utilize the model pre-training pool to minimize edge node training difficulty and employ the approach of secondary edge node training. Finally, extensive experiments confirm the efficacy of our proposed method.
引用
收藏
页数:9
相关论文
共 21 条
[1]   Spatiotemporal analysis of line loss rate: A case study in China [J].
Chen, Xi ;
Song, Chunhe ;
Wang, Tianran .
ENERGY REPORTS, 2021, 7 :7048-7059
[2]   Integration of Edge Computing and Blockchain for Provision of Data Fusion and Secure Big Data Analysis for Internet of Things [J].
Dong, Jingya ;
Song, Chunhe ;
Zhang, Tao ;
Li, Yuanjian ;
Zheng, Hao .
WIRELESS COMMUNICATIONS & MOBILE COMPUTING, 2022, 2022
[3]   Smart grid encounters edge computing: opportunities and applications [J].
Feng, Cheng ;
Wang, Yi ;
Chen, Qixin ;
Ding, Yi ;
Strbac, Goran ;
Kang, Chongqing .
ADVANCES IN APPLIED ENERGY, 2021, 1
[4]   A data-driven multi-model methodology with deep feature selection for short-term wind forecasting [J].
Feng, Cong ;
Cui, Mingjian ;
Hodge, Bri-Mathias ;
Zhang, Jie .
APPLIED ENERGY, 2017, 190 :1245-1257
[5]   Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm [J].
Kotsiopoulos, Thanasis ;
Sarigiannidis, Panagiotis ;
Ioannidis, Dimosthenis ;
Tzovaras, Dimitrios .
COMPUTER SCIENCE REVIEW, 2021, 40
[6]   Designing a short-term load forecasting model in the urban smart grid system [J].
Li, Chen .
APPLIED ENERGY, 2020, 266
[7]   Intelligent Edge Computing for IoT-Based Energy Management in Smart Cities [J].
Liu, Yi ;
Yang, Chao ;
Jiang, Li ;
Xie, Shengli ;
Zhang, Yan .
IEEE NETWORK, 2019, 33 (02) :111-117
[8]   A short-term energy prediction system based on edge computing for smart city [J].
Luo, Haidong ;
Cai, Hongming ;
Yu, Han ;
Sun, Yan ;
Bi, Zhuming ;
Jiang, Lihong .
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2019, 101 :444-457
[9]   Energy management in the smart grid: State-of-the-art and future trends [J].
Meliani, Meryem ;
El Barkany, Abdellah ;
El Abbassi, Ikram ;
Darcherif, Abdel Moumen ;
Mahmoudi, Morad .
INTERNATIONAL JOURNAL OF ENGINEERING BUSINESS MANAGEMENT, 2021, 13
[10]  
SAMIE F, 2019, IoT for Smart Grids, P21, DOI DOI 10.1007/978-3-030-03640-9_2