...
首页> 外文期刊>Solar Energy >Comparative study of Angstroem's and artificial neural networks' methodologies in estimating global solar radiation
【24h】

Comparative study of Angstroem's and artificial neural networks' methodologies in estimating global solar radiation

机译:Angstroem和人工神经网络方法在估计全球太阳辐射中的比较研究

获取原文
获取原文并翻译 | 示例
           

摘要

The aim of the present research is the comparative development of a variety of models for the estimation of solar radiation on a horizontal surface. By using two different methodologies, models of various complexities have been developed and tested. The first methodology refers to the traditional and long-utilized Angstrom's linear approach which is based on measurements of sunshine duration. The second methodology refers to the relatively new approach based on artificial neural networks (ANN) and it can be based on sunshine duration measurements but also on other climatological parameters. Three Angstrom-type models and seven ANN-type models are presented. All of these models are verified against independent data and compared. Lack of sunshine duration measurements renders Angstrom's approach inapplicable; hence the feasibility of applying the ANN models for the calculation of solar radiation in places where there is a lack of sunshine duration measurements is investigated.
机译:本研究的目的是对用于估算水平面上太阳辐射的各种模型进行比较开发。通过使用两种不同的方法,已经开发并测试了各种复杂度的模型。第一种方法是指基于日照持续时间的测量的传统且长期使用的Angstrom线性方法。第二种方法是指基于人工神经网络(ANN)的相对较新的方法,它可以基于日照持续时间的测量值,也可以基于其他气候参数。提出了三种埃斯通模型和七个人工神经网络模型。所有这些模型都针对独立数据进行了验证并进行了比较。缺乏日照时间测量值使Angstrom的方法不适用。因此,研究了在缺乏日照持续时间测量的地方应用ANN模型计算太阳辐射的可行性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号