In this paper, we introduce an innovative thermoelectric, photovoltaic hybrid system and investigate its performance under various radiation intensities and heat transfer coefficients outside the cavity. Our findings reveal that the proposed system yields twice the power output compared to a traditional plate thermoelectric, photovoltaic hybrid system. Through economic analysis, we project a 45 % reduction in energy cost with this novel structure compared to a full hybrid system. Notably, positioning the hybrid system at the bottom of the cavity, where maximum radiation occurs, is deemed optimal. Our heat transfer analysis demonstrates a significant increase in power generation due to convection outside the cavity, with approximately 9 % of incoming radiation reflected and a further 59 % reflected without the cavity. Utilizing artificial neural networks, we predict thermal and electrical power generation, achieving a Mean Absolute Error (MAE) below 3 % and an R-squared value exceeding 0.98. Additionally, our model's predictions closely match experimental results, validating its accuracy and practical utility. This comprehensive study advances the field by offering a novel hybrid system design that outperforms existing solutions while providing insights into optimizing placement and enhancing power generation through sophisticated modeling techniques.

中文翻译：

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通过热电发电机和混合光伏转换器最大限度地提高热效率和电效率：数值、经济和机器学习分析

在本文中，我们介绍了一种创新的热电、光伏混合系统，并研究了其在腔外不同辐射强度和传热系数下的性能。我们的研究结果表明，与传统的板式热电、光伏混合系统相比，所提出的系统的功率输出是其两倍。通过经济分析，我们预计与全混合系统相比，这种新颖的结构可将能源成本降低 45%。值得注意的是，将混合系统放置在发生最大辐射的腔体底部被认为是最佳的。我们的传热分析表明，由于腔体外部的对流，发电量显着增加，大约 9% 的传入辐射被反射，另外 59% 的辐射在没有腔体的情况下被反射。利用人工神经网络，我们预测火力发电和发电量，平均绝对误差 (MAE) 低于 3%，R 平方值超过 0.98。此外，我们模型的预测与实验结果非常吻合，验证了其准确性和实用性。这项全面的研究通过提供一种新颖的混合系统设计来推动该领域的发展，该设计优于现有的解决方案，同时提供了通过复杂的建模技术优化布局和增强发电的见解。