Modelling of solar energy potential in Nigeria using an artificial neural network model
Modelling of solar energy potential in Nigeria using an artificial neural network model
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DOI：
10.1016/j.apenergy.2008.12.005
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发表时间：
2009-09-01
期刊：
影响因子：
11.2
通讯作者：
Fadare, D. A.
中科院分区：
文献类型：
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作者：
Fadare, D. A.
在这项研究中，人工神经网络（ANN）为基础的模型预测太阳能潜力在尼日利亚（拉特。4-14度N，log。2-15（E）已开发。利用MATLAB神经网络工具箱设计了标准的多层前馈反向传播神经网络。培训和测试该网络时使用了美国航天局地球卫星数据库提供的10年（1983-1993年）期间尼日利亚195个城市的地理和气象数据。气象和地理数据（纬度，经度，海拔，月，平均日照时数，平均温度和相对湿度）被用作网络的输入，而太阳辐射强度被用作网络的输出。结果表明，人工神经网络预测和实际的月平均总太阳辐射强度的训练和测试数据集之间的相关系数均高于90%，从而表明在太阳辐射数据不可用的地方的太阳辐射评估模型的可靠性高。用该模型预测的太阳辐射值以月图的形式给出。北方和南方的月平均太阳辐射势分别为7.01-5.62和5.43-3.54 kWh/m2·d。为模型的应用开发了图形用户界面（GUI）。该模型可以很容易地用于太阳能应用的初步设计的太阳辐射估计。(C)2008爱思唯尔有限公司保留所有权利。
In this study, an artificial neural network (ANN) based model for prediction of solar energy potential in Nigeria (lat. 4-14 degrees N, log. 2-15 degrees E) was developed. Standard multilayered, feed-forward, back-propagation neural networks with different architecture were designed using neural toolbox for MATLAB. Geographical and meteorological data of 195 cities in Nigeria for period of 10 years (1983-1993) from the NASA geo-satellite database were used for the training and testing the network. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, mean temperature, and relative humidity) were used as inputs to the network, while the solar radiation intensity was used as the output of the network. The results show that the correlation coefficients between the ANN predictions and actual mean monthly global solar radiation intensities for training and testing datasets were higher than 90%, thus suggesting a high reliability of the model for evaluation of solar radiation in locations where solar radiation data are not available. The predicted solar radiation values from the model were given in form of monthly maps. The monthly mean solar radiation potential in northern and southern regions ranged from 7.01-5.62 to 5.43-3.54 kW h/m(2) day, respectively. A graphical user interface (GUI) was developed for the application of the model. The model can be used easily for estimation of solar radiation for preliminary design of solar applications. (C) 2008 Elsevier Ltd. All rights reserved.