Mapping daily global solar irradiation over Spain: A comparative study of selected approaches

被引:77
作者
Moreno, A. [1 ]
Gilabert, M. A. [1 ]
Martinez, B. [1 ]
机构
[1] Univ Valencia, Dept Fis Terra & Termodinam, E-46100 Valencia, Spain
关键词
Daily global solar irradiation maps; Bristow-Campbell; Artificial Neural Network; Kernel Ridge Regression; ARTIFICIAL NEURAL-NETWORK; AIR-TEMPERATURE; ANCILLARY DATA; RADIATION; MODEL; PRODUCTIVITY; VALIDATION; PREDICTION;
D O I
10.1016/j.solener.2011.05.017
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
Three methods to estimate the daily global solar irradiation are compared: the Bristow-Campbell (BC), Artificial Neural Network (ANN) and Kernel Ridge Regression (KRR). BC is an empirical approach based on air maximum and minimum temperature. ANN and KRR are non-linear approaches that use temperature and precipitation data (which have been selected as the best combination of input data from a gamma test). The experimental dataset includes 4 years (2005-2008) of daily irradiation collected at 40 stations and temperature and precipitation data collected at 400 stations over Spain. Results show that the ANN method produces the best global solar irradiation estimates, with a mean absolute error 2.33 MJ m(-2) day(-1). Daily maps of solar irradiation over Spain at 1-km spatial resolution are produced by applying the ANN method to temperature and precipitation maps generated from ordinary kriging. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码:2072 / 2084
页数:13
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