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基于 BP 神经网络的太阳能光子增强 热电子发电模型.

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Title: 基于 BP 神经网络的太阳能光子增强 热电子发电模型.
Alternate Title: Solar photon-enhanced thermionic emission power generation model based on BP neural network.
Authors: 毕小云1 18560465508@163.com, 李尚达1, 郝梦媛1, 邱浩1 qiuhao@sdu.edu.cn
Source: Clean Coal Technology. Nov2025, Vol. 31 Issue 11, p74-83. 10p.
Subject Terms: *Solar energy, *Thermionic emission, *Economic efficiency, *Electric power production, *Prediction models, *Energy conversion, *Computer simulation, *Back propagation
Abstract (English): Solar energy is characterized by enormous reserves, cleanliness, and low carbon emissions. Vigorously developing solar energy constitutes a key pathway to achieving the "dual carbon" goals. Photon-Enhanced Thermionic Emission (PETE) is an emerging solar power generation technology that couples the photovoltaic effect with the thermionic effect. It boasts advantages such as high efficiency, simple structure, and stable operation, thus demonstrating great development potential. Solar PETE systems are strongly coupled lightheat-electricity systems, and the establishment of their theoretical models is crucial for the development and research of PETE technology. However, the mathematical and physical models for light-heat-electricity coupling typically suffer from high computational costs and low efficiency. A back propagation (BP) neural network model is employed to predict the power generation performance of PETE systems. Three parameters, concentration ratio, electron affinity, and cathode thickness, are selected as input variables, while six key indicators, including cathode temperature and energy conversion efficiency, serve as output targets.A research sample is constructed using 990 sets ofSolar energy is characterized by enormous reserves, cleanliness, and low carbon emissions. Vigorously developing solar energy constitutes a key pathway to achieving the "dual carbon" goals. Photon-Enhanced Thermionic Emission (PETE) is an emerging solar power generation technology that couples the photovoltaic effect with the thermionic effect. It boasts advantages such as high efficiency, simple structure, and stable operation, thus demonstrating great development potential. Solar PETE systems are strongly coupled lightheat-electricity systems, and the establishment of their theoretical models is crucial for the development and research of PETE technology. However, the mathematical and physical models for light-heat-electricity coupling typically suffer from high computational costs and low efficiency. A back propagation (BP) neural network model is employed to predict the power generation performance of PETE systems. Three parameters, concentration ratio, electron affinity, and cathode thickness, are selected as input variables, while six key indicators, including cathode temperature and energy conversion efficiency, serve as output targets.A research sample is constructed using 990 sets of numerical simulation data. The dataset is divided based on the latin hypercube sampling (LHS) method, and logarithmic preprocessing numerical simulation data. The dataset is divided based on the latin hypercube sampling (LHS) method, and logarithmic preprocessing is applied to the photon enhancement coefficient to optimize data distribution. The constructed BP neural network features a node configuration of 3-10-12-6. The Levenberg-Marquardt algorithm is adopted for model training, with two complementary metrics, the coefficient of determination (R²) and mean absolute percentage error (EMAP), used for evaluation. Results show that the model achieves extremely high prediction accuracy for all six parameters, with R2 values exceeding 0.99 and EMAP values approaching 0. Validation using the test set confirms that the model exhibits strong generalization ability. Compared with traditional numerical simulations, the prediction speed is significantly improved, enabling real-time optimization of the system. The effectiveness of the BP neural network model in predicting the performance of solar PETE power generation systems is verified, and an efficient approach for their development and application is provided. [ABSTRACT FROM AUTHOR]
Abstract (Chinese): 太阳能资源巨大、清洁低碳, 大力发展太阳能是实现"双碳"目标的关键路径。光子增强热电 子发射 (Photon-Enhanced Thermionic Emission, PETE) 是一种耦合光伏效应与热电子效应的太阳能 发电新技术, 具有效率高、结构简单、运行稳定等优势, 发展潜力大。太阳能 PETE 是一个光–热–电强 耦合系统, 构建其理论模型是开发和研究 PETE 技术的关键。光–热–电耦合的数学物理模型通常计算 成本高、效率低, 研究采用反向传播 (BP) 神经网络模型预测 PETE 系统发电性能。选取聚光比、电子 亲和势和阴极厚度作为输入参数, 以阴极温度、能量转换效率等 6 项关键指标为输出目标, 利用 990 组 数值模拟数据建立研究样本。采用拉丁超立方试验 (Latin Hypercube Sampling, LHS) 设计划分数据 集, 并对光子增强系数进行对数预处理以优化数据分布。所构建 BP 神经网络结构为 3-10-12-6 节点配 置, 采用 Levenberg-Marquardt 算法进行训练, 并以决定系数 ( R²) 和平均绝对百分比误差 (EMAP) 2 个互补性指标作为评价指标。结果表明: 模型对 6 个参数预测精度极高, R² 在 0.99 以上, 且 EMAP 均 接近 0。经测试集检验, 模型的泛化能力极强。与传统数值模拟相比, 预测速度显著提升, 为系统实时 优化提供了可能。验证了 BP 神经网络模型在太阳能 PETE 发电系统性能预测的有效性, 为其发展和 应用提供了有效途径. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: 基于 BP 神经网络的太阳能光子增强 热电子发电模型.
– Name: TitleAlt
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  Data: Solar photon-enhanced thermionic emission power generation model based on BP neural network.
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  Data: <searchLink fieldCode="AR" term="%22毕小云%22">毕小云</searchLink><relatesTo>1</relatesTo><i> 18560465508@163.com</i><br /><searchLink fieldCode="AR" term="%22李尚达%22">李尚达</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22郝梦媛%22">郝梦媛</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22邱浩%22">邱浩</searchLink><relatesTo>1</relatesTo><i> qiuhao@sdu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Clean+Coal+Technology%22">Clean Coal Technology</searchLink>. Nov2025, Vol. 31 Issue 11, p74-83. 10p.
– Name: Subject
  Label: Subject Terms
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  Data: *<searchLink fieldCode="DE" term="%22Solar+energy%22">Solar energy</searchLink><br />*<searchLink fieldCode="DE" term="%22Thermionic+emission%22">Thermionic emission</searchLink><br />*<searchLink fieldCode="DE" term="%22Economic+efficiency%22">Economic efficiency</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+power+production%22">Electric power production</searchLink><br />*<searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+conversion%22">Energy conversion</searchLink><br />*<searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br />*<searchLink fieldCode="DE" term="%22Back+propagation%22">Back propagation</searchLink>
– Name: Abstract
  Label: Abstract (English)
  Group: Ab
  Data: Solar energy is characterized by enormous reserves, cleanliness, and low carbon emissions. Vigorously developing solar energy constitutes a key pathway to achieving the "dual carbon" goals. Photon-Enhanced Thermionic Emission (PETE) is an emerging solar power generation technology that couples the photovoltaic effect with the thermionic effect. It boasts advantages such as high efficiency, simple structure, and stable operation, thus demonstrating great development potential. Solar PETE systems are strongly coupled lightheat-electricity systems, and the establishment of their theoretical models is crucial for the development and research of PETE technology. However, the mathematical and physical models for light-heat-electricity coupling typically suffer from high computational costs and low efficiency. A back propagation (BP) neural network model is employed to predict the power generation performance of PETE systems. Three parameters, concentration ratio, electron affinity, and cathode thickness, are selected as input variables, while six key indicators, including cathode temperature and energy conversion efficiency, serve as output targets.A research sample is constructed using 990 sets ofSolar energy is characterized by enormous reserves, cleanliness, and low carbon emissions. Vigorously developing solar energy constitutes a key pathway to achieving the "dual carbon" goals. Photon-Enhanced Thermionic Emission (PETE) is an emerging solar power generation technology that couples the photovoltaic effect with the thermionic effect. It boasts advantages such as high efficiency, simple structure, and stable operation, thus demonstrating great development potential. Solar PETE systems are strongly coupled lightheat-electricity systems, and the establishment of their theoretical models is crucial for the development and research of PETE technology. However, the mathematical and physical models for light-heat-electricity coupling typically suffer from high computational costs and low efficiency. A back propagation (BP) neural network model is employed to predict the power generation performance of PETE systems. Three parameters, concentration ratio, electron affinity, and cathode thickness, are selected as input variables, while six key indicators, including cathode temperature and energy conversion efficiency, serve as output targets.A research sample is constructed using 990 sets of numerical simulation data. The dataset is divided based on the latin hypercube sampling (LHS) method, and logarithmic preprocessing numerical simulation data. The dataset is divided based on the latin hypercube sampling (LHS) method, and logarithmic preprocessing is applied to the photon enhancement coefficient to optimize data distribution. The constructed BP neural network features a node configuration of 3-10-12-6. The Levenberg-Marquardt algorithm is adopted for model training, with two complementary metrics, the coefficient of determination (R²) and mean absolute percentage error (EMAP), used for evaluation. Results show that the model achieves extremely high prediction accuracy for all six parameters, with R2 values exceeding 0.99 and EMAP values approaching 0. Validation using the test set confirms that the model exhibits strong generalization ability. Compared with traditional numerical simulations, the prediction speed is significantly improved, enabling real-time optimization of the system. The effectiveness of the BP neural network model in predicting the performance of solar PETE power generation systems is verified, and an efficient approach for their development and application is provided. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label: Abstract (Chinese)
  Group: Ab
  Data: 太阳能资源巨大、清洁低碳, 大力发展太阳能是实现"双碳"目标的关键路径。光子增强热电 子发射 (Photon-Enhanced Thermionic Emission, PETE) 是一种耦合光伏效应与热电子效应的太阳能 发电新技术, 具有效率高、结构简单、运行稳定等优势, 发展潜力大。太阳能 PETE 是一个光–热–电强 耦合系统, 构建其理论模型是开发和研究 PETE 技术的关键。光–热–电耦合的数学物理模型通常计算 成本高、效率低, 研究采用反向传播 (BP) 神经网络模型预测 PETE 系统发电性能。选取聚光比、电子 亲和势和阴极厚度作为输入参数, 以阴极温度、能量转换效率等 6 项关键指标为输出目标, 利用 990 组 数值模拟数据建立研究样本。采用拉丁超立方试验 (Latin Hypercube Sampling, LHS) 设计划分数据 集, 并对光子增强系数进行对数预处理以优化数据分布。所构建 BP 神经网络结构为 3-10-12-6 节点配 置, 采用 Levenberg-Marquardt 算法进行训练, 并以决定系数 ( R²) 和平均绝对百分比误差 (EMAP) 2 个互补性指标作为评价指标。结果表明: 模型对 6 个参数预测精度极高, R² 在 0.99 以上, 且 EMAP 均 接近 0。经测试集检验, 模型的泛化能力极强。与传统数值模拟相比, 预测速度显著提升, 为系统实时 优化提供了可能。验证了 BP 神经网络模型在太阳能 PETE 发电系统性能预测的有效性, 为其发展和 应用提供了有效途径. [ABSTRACT FROM AUTHOR]
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        Value: 10.13226/j.issn.1006-6772.SUN25072801
    Languages:
      – Code: chi
        Text: Chinese
    PhysicalDescription:
      Pagination:
        PageCount: 10
        StartPage: 74
    Subjects:
      – SubjectFull: Solar energy
        Type: general
      – SubjectFull: Thermionic emission
        Type: general
      – SubjectFull: Economic efficiency
        Type: general
      – SubjectFull: Electric power production
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Energy conversion
        Type: general
      – SubjectFull: Computer simulation
        Type: general
      – SubjectFull: Back propagation
        Type: general
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      – TitleFull: 基于 BP 神经网络的太阳能光子增强 热电子发电模型.
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            NameFull: 毕小云
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            NameFull: 李尚达
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            NameFull: 郝梦媛
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            – D: 01
              M: 11
              Text: Nov2025
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              Y: 2025
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            – TitleFull: Clean Coal Technology
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