Estimation of wind velocity over a complex terrain using the Generalized Mapping Regressor

被引：30

作者：

Beccali, M. ^{[1
]}

Cirrincione, G. ^{[2
]}

Marvuglia, A. ^{[1
]}

Serporta, C. ^{[3
]}

机构：

[1] Univ Palermo, DREAM, I-90128 Palermo, Italy

[2] Univ Picardie Jules Verne, Dept Genie Elect, F-80039 Amiens, France

[3] ISSIA CNR Inst Intelligent Syst Automat, Sect Palermo, Palermo, Italy

来源：

APPLIED ENERGY | 2010年 / 87卷 / 03期

关键词：

Neural networks; Generalized Mapping Regressor; Curvilinear Component Analysis; Wind; Sicily; SPEED SPATIAL ESTIMATION; MASS-CONSISTENT MODELS;

D O I：

10.1016/j.apenergy.2009.05.026

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：

Wind energy evaluation is an important goal in the conversion of energy systems to more environmentally friendly solutions. In this paper, we present a novel approach to wind speed spatial estimation on the isle of Sicily (Italy): an incremental self-organizing neural network (Generalized Mapping Regressor - GMR) is coupled with exploratory data analysis techniques in order to obtain a map of the spatial distribution of the average wind speed over the entire region. First, the topographic surface of the island was modelled using two different neural techniques and by exploiting the information extracted from a digital elevation model of the region. Then, GMR was used for automatic modelling of the terrain roughness. Afterwards, a statistical analysis of the wind data allowed for the estimation of the parameters of the Weibull wind probability distribution function. In the last sections of the paper, the expected values of the Weibull distributions were regionalized using the GMR neural network. (C) 2009 Elsevier Ltd. All rights reserved.

引用

页码：884 / 893

页数：10

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