Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia

Imran Tasadduq, Shafiqur Rehman and Khaled Bubshait

Renewable Energy, 2002, vol. 25, issue 4, 545-554

Abstract: This paper utilizes artificial neural networks for the prediction of hourly mean values of ambient temperature 24 h in advance. Full year hourly values of ambient temperature are used to train a neural network model for a coastal location — Jeddah, Saudi Arabia. This neural network is trained off-line using back propagation and a batch learning scheme. The trained neural network is successfully tested on temperatures for years other than the one used for training. It requires only one temperature value as input to predict the temperature for the following day for the same hour. The predicted hourly temperature values are compared with the corresponding measured values. The mean percent deviation between the predicted and measured values is found to be 3.16, 4.17 and 2.83 for three different years. These results testify that the neural network can be a valuable tool for hourly temperature prediction in particular and other meteorological predictions in general.

Keywords: Neural networks; Back propagation; Prediction; Temperature; Meteorology; Batch learning; Pattern learning (search for similar items in EconPapers)
Date: 2002
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Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:25:y:2002:i:4:p:545-554

DOI: 10.1016/S0960-1481(01)00082-9

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