CBGA: A deep learning method for power grid communication networks service activity prediction

Author:

Liu Shangdong,Zhou Longfei,Shao Sisi,Zuo Jun,Ji Yimu

Funder

Key Project of Natural Science and University Natural Science of Jiangsu Province

Open Research Project of Zhejiang Lab

Publisher

Springer Science and Business Media LLC

Reference37 articles.

1. Ahmed S, Gondal TM, Adil M, Malik SA, Qureshi R (2019) A survey on communication technologies in smart grid. In: 2019 IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia), IEEE, pp 7–12

2. Yingjun W, Chen J, Yingtao R, Hao X, Roger M, Ni M (2020) Research on power communication network planning based on information transmission reachability against cyber-attacks. IEEE Syst J 15(2):2883–2894

3. Dehghanpour K, Wang Z, Wang J, Yuan Y, Fankun B (2018) A survey on state estimation techniques and challenges in smart distribution systems. IEEE Trans Smart Grid 10(2):2312–2322

4. Zhang Y, Wang J, Li Z (2019) Uncertainty modeling of distributed energy resources: techniques and challenges. Curr Sustain Renew Energy Rep 6:42–51

5. Lin J, Ma J, Zhu J, Cui Yu (2022) Short-term load forecasting based on LSTM networks considering attention mechanism. Int J Electr Power Energy Syst 137:107818

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