Abstract
In modern power systems, it is an important issue to process and analyze power big data and perform reliable decision-making analysis. In response to this problem, this paper proposes a distributed computing architecture for power data based on a consortium chain, which realizes distributed and trusted shared training computing for power data while taking into account the privacy protection of the original data. To solve the problem of sample imbalance, this paper proposes a data balancing method combining SMOTE algorithm and the k-means algorithm. This paper also proposes an LSTM neural network load forecasting method based on federated learning and proves that it has higher accuracy and applicability than traditional methods through examples.
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Acknowledgments
This paper is supported by the science and technology project of State Grid Jibei Information & Telecommunication Company “Trusted Sharing Technology and Application of Winter Olympic Power Data Based on Blockchain” (52018E20008J).
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© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Li, X., Shang, F., Yao, Y., Zheng, T. (2022). Power Data Credible Decision-Making Mechanism Based on Federated Learning and Blockchain. In: Fang, F., Shu, F. (eds) Game Theory for Networks. GameNets 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-23141-4_6
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DOI: https://doi.org/10.1007/978-3-031-23141-4_6
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