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Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia |
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| Authors: | Imran Tasadduq Shafiqur Rehman Khaled Bubshait |
| Affiliation: | 1. Center for Economics and Management Systems, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia;2. Center for Engineering Research, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia;1. Energy Efficiency in Buildings R&D Unit, CIEMAT, Avenida Complutense n°40, Madrid 28040, Spain;2. Institute for Energy and Transport - Renewable Energy Unit, JRC, Via E. Fermi 2749, TP 450, Ispra 21027, Italy;1. Department of Management Science and Engineering, Business School, Ningbo University, No. 818, Fenghua Road, Ningbo City, Zhejiang Province 315211, China;2. Department of Business Administration, Chung Yuan Christian University, No. 200, Chung-Pei Road, Chung-Li District, Taoyuan City, Taiwan 32023, ROC;1. Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL 32611, United States;2. Citrus Research and Education Center, University of Florida, Lake Alfred, FL 33850, United States |
| 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 |
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