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Improvement of temperature-based ANN models for solar radiation estimation through exogenous data assistance

机译：通过外源数据辅助改进基于温度的ANN模型用于太阳辐射估计

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摘要

The development of new and more precise temperature-based models for solar radiation estimation is decisive, given the immediacy and simplicity associated to their input measurements and the ubiquitous problems derived from equipment failures, maintenance and calibration, and physical and biological constraints. Further, the performance quality of empirical equations is to be questioned in a large variety of climatic contexts. As an alternative to traditional techniques, artificial neural networks (ANNs) are highly appropriate for the modelling of non-linear processes. Nevertheless, temperature-based ANN models do not always provide accurate enough solar radiation estimations as their performance depends considerably on the specific temperature/solar radiation relationships of the studied context. This paper describes a new procedure to improve the performance accuracy of temperature-based ANN models for estimation of total solar radiation on a horizontal surface (R_s) taking advantage of ancillary data records from secondary similar stations, which work as exogenous inputs. The influence on the model performance of the number of considered ancillary stations and the corresponding number of training patterns is also analyzed. Finally, these models are compared with those relying exclusively on local temperature recordings. The proposed models provide performances with lower associated errors than those which do not consider exogenous inputs. The ancillary supply is translated into a decrease around 0.1 of RMSE in the local performance. The consideration of non-measured inputs in the simple local temperature-based models, namely extraterrestrial radiation or day of the year, entails a performance accuracy improvement around 0.1 of RMSE.
机译：考虑到与它们的输入测量相关的直接性和简便性，以及由于设备故障，维护和校准以及物理和生物学限制而引起的普遍问题，开发新的且更精确的基于温度的太阳辐射估算模型具有决定性意义。此外，经验方程的性能质量将在多种气候环境中受到质疑。作为传统技术的替代方法，人工神经网络（ANN）非常适合于非线性过程的建模。然而，基于温度的人工神经网络模型并不总是能够提供足够准确的太阳辐射估计，因为它们的性能在很大程度上取决于所研究环境的特定温度/太阳辐射关系。本文介绍了一种新的程序，该程序可以提高基于温度的人工神经网络模型的性能精度，该模型可利用来自次级相似台站（作为外源输入）的辅助数据记录来估算水平面上的总太阳辐射（R_s）。还分析了所考虑的辅助站点数量和相应的训练模式对模型性能的影响。最后，将这些模型与仅依赖于本地温度记录的模型进行比较。与不考虑外源输入的模型相比，所提出的模型所提供的性能具有更低的相关误差。辅助供应转换为本地性能的RMSE降低约0.1。在简单的基于局部温度的模型中考虑未测量的输入，即地外辐射或一年中的某天，需要将性能精度提高大约RMSE 0.1。

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  • 来源
    《Energy Conversion & Management》 |2011年第2期|p.990-1003|共14页
  • 作者

    Pau Marti; Maria Gasque;

  • 作者单位

    Departamento de Ingenieria Rural y Agmalimentaria, Universidad Politecnica de Valencia. Cami de Vera s, 46022 Valencia, Spain;

    Departamento de Fisica Aplicada, Universidad Polkecnica de Valencia. Cami de Vera s, 46022 Valencia, Spain;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    artificial neural networks; solar radiation; exogenous variables;

    机译：人工神经网络;太阳辐射;外生变量;

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