ANN-based modelling and estimation of daily global solar radiation data: A case study

被引：182

作者：

Benghanem, M. ^{[1
]}

Mellit, A. ^{[2
]}

Alamri, S. N. ^{[1
]}

机构：

[1] Taibah Univ, Fac Sci, Dept Phys, Medina, Saudi Arabia

[2] Jijel Univ, LAMEL, Fac Engn Sci, Dept LMD Elect, Jijel 18000, Algeria

来源：

ENERGY CONVERSION AND MANAGEMENT | 2009年 / 50卷 / 07期

关键词：

Global solar radiation; Correlation; Modelling; Estimation; Neural networks; ARTIFICIAL NEURAL-NETWORKS;

D O I：

10.1016/j.enconman.2009.03.035

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

In this paper, an artificial neural network (ANN) models for estimating and modelling of daily global solar radiation have been developed. The data used in this work are the global irradiation H-G, diffuse irradiation H-D, air temperature T and relative humidity H-u. These data are available from 1998 to 2002 at the National Renewable Energy Laboratory (NREL) website. We have developed six ANN-models by using different combination as inputs: the air temperature, relative humidity, sunshine duration and the day of year. For each model. the output is the daily global solar radiation. Firstly, a set of 4 x 365 points (4 years) has been used for training each networks while a set of 365 points (1 year) has been used for testing and validating the ANN-models. It was found that the model using sunshine duration and air temperature as inputs, gives good accurate results since the correlation coefficient is 97.65%. A comparative study between developed ANN-models and conventional regression models is presented in this study. (C) 2009 Elsevier Ltd. All rights reserved.

引用

页码：1644 / 1655

页数：12

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