A Prediction Optimization Method with Federated Learning and Neural Architecture Search for Distributed Renewable Energy Sources
DOI:
https://doi.org/10.13052/dgaej2156-3306.4021Keywords:
Distributed energy system, predictive optimization, federated learning, neural architecture search, adaptive controlAbstract
This paper proposes a prediction optimization method with federated learning and neural architecture search for distributed renewable energy sources, which is suitable for intelligent management of multi node distributed energy networks. The federated learning technology is used to enable each energy node to independently train local neural network models without sharing raw data, ensuring data privacy and security, and achieving collaborative optimization among nodes through the aggregation of global model parameters. By combining neural architecture search (NAS) technology, the system can automatically design the optimal neural network architecture and dynamically adapt to the energy demands and environmental changes of different nodes. This method improves the efficiency and robustness of distributed renewable energy systems through real-time scheduling optimization and load forecasting, and is suitable for various renewable energy generation scenarios such as wind power and photovoltaics. Finally, the experiment results have shown that the method proposed in this paper improves the intelligence of energy scheduling and the accuracy of prediction, ensuring data security and the adaptive optimization capability of the system.
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