Abdoulhdi A. Borhana
Universiti Tenaga Nasional

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Journal : Indonesian Journal of Electrical Engineering and Computer Science

Daily predictions of solar radiation utilizing genetic programming techniques Rahima Ummi Kulsum Nadya; Ali Najah Ahmed; Abdoulhdi A. Borhana; N. A. Mardhiah; Amr El-Shafie; Ahmed El-Shafie
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 2: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i2.pp900-905

Abstract

The solar radiation prediction in Kuala Terengganu located in Terengganu, Malaysia was investigated in this study to improve the solar system design. Solar radiation data and number of parameters such as solar radiation, temperature, humidity, wind speed and sunshine hours were obtained from Malaysian Meteorological Malaysia MMD. In order to predict the solar radiation, Genetic Programming Techniques (GP) models were develop and tested. Two scenarios were considered in this study in order to validate the efficiency of the proposed model. Coefficients of determination (R2) for the solar radiation during training and testing phases were ranged between 0.99402 to 0.98934 for all months of the year. This study confirms the ability of GP to predict solar radiation values precisely and accurately. The predictions from the GP models could enable scientists to locate and design solar energy systems in Malaysia.
Ozone prediction based on support vector machine M. Tanaskuli; Ali N. Ahmed; Nuratiah Zaini; Samsuri Abdullah; Abdoulhdi A. Borhana; N. A. Mardhiah; Mathivanan Mathivanan
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 3: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i3.pp1461-1466

Abstract

The prediction of tropospheric ozone concentrations is very important due to negative effects of ozone on human health, atmosphere and vegetation. Ozone Prediction is an intricate procedure and most of the conventional models cannot provide accurate prediction. Machine Learning techniques have been widely used as an effective tool for prediction. This study is investigating the implementation of Support vector Machine-SVM to predict Ozone concentrations. The results show that the SVM is capable in predicting ozone concentrations with acceptable level of accuracy. Sensitivity analysis has been conducted to show what is the most effective parameters on the proposed model.