Photovoltaic-Thermoelectric Generator (PV-TEG) system has emerged as a promising approach to significantly enhance the efficiency of conventional PV cells. However, optimizing the performance of these hybrid systems presents a formidable challenge due to their complex structure and multitude of design parameters. This study tackles such challenge by developing a machine learning based Artificial Neural Network (ANN) model which comprises two sub-ANN models that can work independently for PV and TEG modules or in combination through a cyclic approach for the hybrid PV-TEG system. The model demonstrates remarkable versatility, allowing control over various parameters such as PV coating, device geometry, and environmental conditions. Compared to COMSOL simulations, the ANN model achieves over 97.6 % accuracy with a 6000-fold increase in simulation speed, enabling extensive parameter sweeps and insightful system analysis. Within 18 min, the model conducted a real-time simulation using 8712 weather data entries from Singapore in 2022 and predicted that the hybrid PV-TEG system would generate a total power of 265 kWh/m, 6.4 % more than that of the standalone PV system with an average system temperature reduction of 7 K. The model's rapid processing capabilities and high accuracy are particularly beneficial for large-scale simulations and practical applications in renewable energy technology.

中文翻译：

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人工神经网络支持光伏热电发电机建模和分析


光伏热电发电机（PV-TEG）系统已成为显着提高传统光伏电池效率的一种有前景的方法。然而，由于其复杂的结构和众多的设计参数，优化这些混合系统的性能面临着巨大的挑战。本研究通过开发基于机器学习的人工神经网络 (ANN) 模型来解决这一挑战，该模型由两个子 ANN 模型组成，这些模型可以独立用于 PV 和 TEG 模块，也可以通过混合 PV-TEG 系统的循环方法组合工作。该模型展示了卓越的多功能性，可以控制各种参数，例如光伏涂层、器件几何形状和环境条件。与 COMSOL 仿真相比，ANN 模型的准确度超过 97.6%，仿真速度提高了 6000 倍，能够进行广泛的参数扫描和富有洞察力的系统分析。模型在 18 分钟内利用新加坡 2022 年的 8712 个天气数据进行实时模拟，预测混合 PV-TEG 系统的总发电量为 265 kWh/m，比独立光伏发电增加 6.4%系统平均温度降低7 K。该模型的快速处理能力和高精度特别有利于可再生能源技术的大规模模拟和实际应用。