Artificial neural network estimation of global solar radiation using air temperature and relative humidity. Energy Policy.
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Abstract
Measured air temperature and relative humidity values between 1998 and 2002 for Abha city in Saudi Arabia were used for the estimation of global solar radiation (GSR) in future time domain using artificial neural network method. The estimations of GSR were made using three combinations of data sets namely; (i) day of the year and daily maximum air temperature as inputs and GSR as output, (ii) day of the year and daily mean air temperature as inputs and GSR as output and (iii) time day of the year, daily mean air temperature and relative humidity as inputs and GSR as output. The measured data between 1998 and 2001 were used for training the neural networks while the remaining 240 days' data from 2002 as testing data. The testing data were not used in training the neural networks. Obtained results show that neural networks are well capable of estimating GSR from temperature and relative humidity. This can be used for estimating GSR for locations where only temperature and humidity data are available.
| Item Type: | Article |
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| Subjects: | Research > Engineering |
| Department: | College of Engineering and Physics > Electrical Engineering |
| Depositing User: | ASAD UR RAHMAN |
| Date Deposited: | 11 Jul 2009 12:10 |
| Last Modified: | 01 Aug 2021 11:36 |
| URI: | http://eprints.kfupm.edu.sa/id/eprint/136071 |