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

被引:220
作者
Tymvios, FS
Jacovides, CP
Michaelides, SC
Scouteli, C
机构
[1] Meteorol Serv, CY-1418 Nicosia, Cyprus
[2] Univ Athens, Dept Phys, Div Appl Phys, Lab Meteorol, Athens 15784, Greece
[3] Univ Cyprus, Dept Comp Sci, CY-1678 Nicosia, Cyprus
关键词
solar radiation; neural nets; Angstrom;
D O I
10.1016/j.solener.2004.09.007
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
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. (c) 2004 Elsevier Ltd. All rights reserved.
引用
收藏
页码:752 / 762
页数:11
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