Editorial
Power optimization and control strategy for new energy hybrid power generation system based on deep learning
@ARTICLE{10.4108/ew.7115, author={Fei Li}, title={Power optimization and control strategy for new energy hybrid power generation system based on deep learning}, journal={EAI Endorsed Transactions on Energy Web}, volume={12}, number={1}, publisher={EAI}, journal_a={EW}, year={2025}, month={4}, keywords={new energy hybrid power generation system, Deep learning, Power optimization, Control strategy, Neural network model}, doi={10.4108/ew.7115} }- Fei Li
Year: 2025
Power optimization and control strategy for new energy hybrid power generation system based on deep learning
EW
EAI
DOI: 10.4108/ew.7115
Abstract
The continuous shortage of non-renewable energy and the increasingly serious environmental pollution have made clean renewable energy represented by wind and solar energy become the focus of attention. This paper mainly studies the power optimization and control strategy of a new energy hybrid power generation system based on deep learning. This paper introduces the basic principle and structure of a new energy hybrid power generation system and the application of deep learning technology in power optimization and control strategy. In this paper, a power optimization method based on deep learning is proposed, which realizes real-time optimization of power generation system powers by training neural network models. A control strategy based on deep learning is designed to improve the stability and efficiency of the power generation system. The effectiveness of the proposed method in practical application is verified by experiments.
Copyright © 2025 F. Li et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
