Research Article
Power Data Credible Decision-Making Mechanism Based on Federated Learning and Blockchain
@INPROCEEDINGS{10.1007/978-3-031-23141-4_6, author={Xin Li and Fangjian Shang and Yanli Yao and Tianren Zheng}, title={Power Data Credible Decision-Making Mechanism Based on Federated Learning and Blockchain}, proceedings={Game Theory for Networks. 11th International EAI Conference, GameNets 2022, Virtual Event, July 7--8, 2022, Proceedings}, proceedings_a={GAMENETS}, year={2023}, month={1}, keywords={Power data Federated learning Blockchain LSTM}, doi={10.1007/978-3-031-23141-4_6} }- Xin Li
Fangjian Shang
Yanli Yao
Tianren Zheng
Year: 2023
Power Data Credible Decision-Making Mechanism Based on Federated Learning and Blockchain
GAMENETS
Springer
DOI: 10.1007/978-3-031-23141-4_6
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.
