Power consumption prediction model and method based on federated learning

Anqin Luo; Fan Xie; Jianan Yuan; Nan Zhang

doi:10.1117/12.2679221

25 May 2023 Power consumption prediction model and method based on federated learning

Anqin Luo, Fan Xie, Jianan Yuan, Nan Zhang

Proceedings Volume 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023); 1271219 (2023) https://doi.org/10.1117/12.2679221
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 2023, Huzhou, China

Abstract

There is a serious data island problem for many power data sets at present, and centralized storage of large amounts of data will cause the privacy disclosure of the original data owner, and also face security and regulatory requirements. This patent proposes a power consumption prediction model and method based on federated learning, which includes cloud computing center, central node, edge computing node and communication network. The cloud computing center builds an improved LSTM based power consumption prediction model and initializes it. The central node publishes prediction tasks and cloud computing publishes initialization models to edge computing nodes. After receiving the prediction tasks and initialization models, edge computing nodes use local load data to perform local training. After training, the central node receives the training parameters of each edge computing node, The root mean square error and average relative error of the parameters are calculated. If the error is less than the set accuracy threshold, the new model will be sent to the edge node to complete the model training. If the error is greater than the set accuracy threshold, the local model update parameters of the edge node will continue to perform the training task and perform the iterative training task, which improves the accuracy of power consumption prediction, and isolates the data to ensure that the data will not be leaked to the outside, further meeting the needs of user privacy protection and data security.

Citation Download Citation

Anqin Luo, Fan Xie, Jianan Yuan, and Nan Zhang "Power consumption prediction model and method based on federated learning", Proc. SPIE 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 1271219 (25 May 2023); https://doi.org/10.1117/12.2679221

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KEYWORDS

Data modeling

Power consumption

Machine learning

Education and training

Cloud computing

Data communications

Data storage

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