An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation

S.M. Al-Alawi and H.A. Al-Hinai

Renewable Energy, 1998, vol. 14, issue 1, 199-204

Abstract: In this work, a novel approach using an artificial neural network was used to develop a model for analyzing the relationship between the Global Radiation (GR) and climatological variables, and to predict GR for locations not covered by the model's training data. The predicted global radiation values for the different locations (for different months) were then compared with the actual values. Results indicate that the model predicted the Global Radiation values with a good accuracy of approximately 93% and a mean absolute percentage error of 7.30. In addition, the model was also tested to predict GR values for the Seeb location over a 12 month period. The monthly predicted values of the ANN model compared to the actual GR values for Seeb produced an accuracy of 95% and a mean absolute percentage error of 5.43. Data for these locations were not included as part of ANN training data. Hence, these results demonstrate the generalization capability of this novel approach over unseen data and its ability to produce accurate estimates. Finally, this ANN-based model was also used to predict the global radiation values for Majees, a new location in north Oman.

Keywords: Global Radiation; Solar Radiation; Artificial Neural Network; Prediction; Forecasting (search for similar items in EconPapers)
Date: 1998
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (44)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148198000688
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:14:y:1998:i:1:p:199-204

DOI: 10.1016/S0960-1481(98)00068-8

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().