Deep learning method based on graph neural network for performance prediction of supercritical CO2 power systems

•A model using graph neural network is proposed to predict state points and performance metrics.•A configuration representation method based on thermodynamic graph is developed.•GNN can extract structure features from different graphs of three SCO2 power systems.•GNN achieves excellent accuracy and...

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Bibliographic Details
Published in:Applied energy 2022-10, Vol.324, p.119739, Article 119739
Main Authors: Sun, Lei, Liu, Tianyuan, Wang, Ding, Huang, Chengming, Xie, Yonghui
Format: Article
Language:English
Subjects:
carbon dioxide
data collection
Digital twin
enthalpy
entropy
exergy
Graph neural network
greenhouse gases
neural networks
normal distribution
Performance prediction
prediction
regression analysis
S-CO2 power system
support vector machines
temperature
Thermodynamic characteristics
topology
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Summary:•A model using graph neural network is proposed to predict state points and performance metrics.•A configuration representation method based on thermodynamic graph is developed.•GNN can extract structure features from different graphs of three SCO2 power systems.•GNN achieves excellent accuracy and efficiency and outperforms classical data-driven models. Considering the increasing energy consumption and greenhouse gas emissions, the Supercritical CO2 (S-CO2) power system has attracted more and more attention. Due to the expensive computation resource and time cost, data-based solutions for performance prediction are urged. The surrogate model by machine learning is a promising alternative, but it only focuses on the objective functions and ignores the importance of topological structures and physical states of cycles. Aiming at providing a comprehensive model to predict physical states as well as thermodynamic characteristics, a deep learning method based on graph neural network (GNN) are devised in this paper. With the modeling calculation results as training dataset, a well-trained model can accurately reconstruct the physical states consisting of temperature, pressure, enthalpy, entropy (relative error of most samples
ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2022.119739