Abstract
The present study utilizes the radial basis functions technique for the estimation of monthly mean daily values of solar radiation falling on horizontal surfaces and compares its performance with that of the multilayer perceptrons network and a classical regression model. In this work, we use solar radiation data from 41 stations that are spread over the Kingdom of Saudi Arabia. The solar radiation data from 31 locations are used for training the neural networks and the data from the remaining 10 locations are used for testing the estimated values. However, the testing data were not used in the modeling or training of the networks to give an indication of the performance of the system at unknown locations. Results indicate the viability of the radial basis for this kind of problem.
| Original language | English |
|---|---|
| Pages (from-to) | 161-168 |
| Number of pages | 8 |
| Journal | Solar Energy |
| Volume | 68 |
| Issue number | 2 |
| DOIs | |
| State | Published - Feb 2000 |
Bibliographical note
Funding Information:The authors wish to acknowledge the support of King Fahd University of Petroleum & Minerals. Also, the authors would like to thank the reviewers for their constructive comments.
ASJC Scopus subject areas
- Renewable Energy, Sustainability and the Environment
- General Materials Science