Analysis on innovation management of power financial transaction strategy integrating BO-BERT-GRNN model

This paper addresses the innovation management problem of financial trading strategies for power system planning through the utilization of the BO-BERT-GRNN model. The BO-BERT-GRNN model, which combines Bayesian optimization, BERT model, and gated recurrent neural network, is divided into three part...

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Bibliographic Details
Published in:Frontiers in energy research 2023-10, Vol.11
Main Authors: Zhang, Mengzhu, Shen, Ling, Guo, Jiaqi
Format: Article
Language:English
Subjects:
BERT
financial trading strategies innovation Frontiers
GRNN
intelligent grid
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Summary:This paper addresses the innovation management problem of financial trading strategies for power system planning through the utilization of the BO-BERT-GRNN model. The BO-BERT-GRNN model, which combines Bayesian optimization, BERT model, and gated recurrent neural network, is divided into three parts to optimize hyperparameters, extract features from historical data, and model and predict power system planning. The objective is to achieve electricity asset allocation, market risk management, and revenue maximization. Experimental analysis demonstrates that the BO-BERT-GRNN model outperforms in power system planning price prediction, energy transaction risk management, and energy asset allocation, showcasing its potential for practical application. This paper addresses the innovation management problem of financial trading strategies for power system planning through the utilization of the BO-BERT-GRNN model. The BO-BERT-GRNN model, which combines Bayesian optimization, BERT model, and gated recurrent neural network, is divided into three parts to optimize hyperparameters, extract features from historical data, and model and predict power system planning. The objective is to achieve electricity asset allocation, market risk management, and revenue maximization. Experimental analysis demonstrates that the BO-BERT-GRNN model outperforms in power system planning price prediction, energy transaction risk management, and energy asset allocation, showcasing its potential for practical application.
ISSN:2296-598X
2296-598X
DOI:10.3389/fenrg.2023.1269059