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Estimating Ambient Temperature for Malaysia Using Generalized Regression Neural Network

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Sun, 2015-01-25 15:32 — Marwan Mahmoud
Journal Title, Volume, Page: 
International Journal of Green Energy. 04/2012; DOI: 10.1080/15435075.2011.621473
Year of Publication: 
2012
Link: 
http://www.tandfonline.com/doi/abs/10.1080/15435075.2011.621473#.VMTl2S7-mlE
Authors: 
Tamer Khatib
Department of Electrical, Electronic & System Engineering, Faculty of Engineering & Built Environment , Universiti Kebangsaan Malaysia , Bangi , Selangor , Malaysia
Azah Mohamed
Department of Electrical, Electronic & System Engineering, Faculty of Engineering & Built Environment , Universiti Kebangsaan Malaysia , Bangi , Selangor , Malaysia
K. Sopian
Solar Energy Research Institute, Universiti Kebangsaan Malaysia , Bangi , Selangor , Malaysia
M. Mahmoud
Department of Electrical Engineering, Faculty of Engineering and Information Technology, An-Najah National University, Nablus. Palestine
Current Affiliation: 
Department of Electrical Engineering, Faculty of Engineering and Information Technology, An-Najah National University, Nablus. Palestine
[email protected]
Preferred Abstract (Original): 

This paper presents a new method for predicting hourly ambient temperature series for Malaysia using generalized regression neural network, GRNN. MATLAB was used to develop the GRNN using the weather records for Malaysia. The developed model has five inputs and one output. The inputs of the proposed model are hour, day, month, sun shine ratio and relative humidity, meanwhile ambient temperature is the output. To evaluate the accuracy of the GRNN, three statistical parameters, namely, the mean absolute percentage error, MAPE, mean bias error, MBE, and root mean square error, RMSE are considered. The GRNN results give an accurate prediction of ambient temperatures for the selected for testing months with average values of MAPE, MBE and RMSE of 2.65%, 4.05% and 0.347%, respectively. The advantage of the proposed method is that it is able to predict ambient temperature at sites where there is no ambient temperature measuring instrument installed.