1
Al-Furat Al-Awsat Technical University, Technical Institute of Najaf, Iraq
(noor.hachame@atu.edu.iq)
2
Department of System Programming, South Ural State University, Chelyabinsk 454080, Russia
(alkattan.hussein92@gmail.com)
3
School of Architecture, Planning, and Environmental Policy & CeADAR, University College Dublin, Dublin, Ireland
(hamid.rabiei@UCD.ie)
4
Electronics and Communications Engineering Department, Faculty of Engineering, Delta University for Science and Technology, Gamasa City 11152, Egypt
(Mohamed.saber@deltauniv.edu.eg)
5
Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, Egypt
(mmm@ieee.org)
Abstract :
Solar radiation constitutes the Earth’s primary energy source and is critical in regulating surface radiation equilibrium, vegetation photosynthesis, hydrological cycles, and extreme atmospheric. On the other hand, the depletion of global fossil fuel reserves mandates the power sector to adopt renewable energy-based sources, including photovoltaic and wind energy conversion systems. Therefore, the precise solar radiation prediction is imperative for climate research and the solar industry. This paper illustrates the use of two machine-learning approaches: random forest (RF) and support vector machine (SVM), to predict surface solar radiation in the Diyala governorate of Iraq for one step ahead, utilizing only lagged monthly time series data of the factor as input predictors. The findings were evaluated using three performance measures: coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The results showed that using 10 monthly lags time series as input predictors leads to the best prediction performance. Furthermore, in terms of the RMSE, the prediction performance of the RF algorithm was better than that of the SVM algorithm (RF's RMSE, MAE, and R2 were 181.398, 129.522, and 0.979, while for SVM were 240.149, 184.802, and 0.978, respectively).
Keywords :
random forest; support vector machine; machine learning; solar radiation prediction
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| Style | # |
|---|---|
| MLA | Noor Razzaq Abbas , Hussein Alkattan , Hamidreza Rabiei-Dastjerdi , Mohamed Saber, Marwa M. Eid. "Monthly Solar Prediction Using Machine Learning: Diyala Governorate, Iraq as a Case Study." Journal of Artificial Intelligence and Metaheuristics, Vol. 5, No. 2, 2023 ,PP. 41-46 (Doi : https://doi.org/10.54216/JAIM.050204) |
| APA | Noor Razzaq Abbas , Hussein Alkattan , Hamidreza Rabiei-Dastjerdi , Mohamed Saber, Marwa M. Eid. (2023). Monthly Solar Prediction Using Machine Learning: Diyala Governorate, Iraq as a Case Study. Journal of Journal of Artificial Intelligence and Metaheuristics, 5 ( 2 ), 41-46 (Doi : https://doi.org/10.54216/JAIM.050204) |
| Chicago | Noor Razzaq Abbas , Hussein Alkattan , Hamidreza Rabiei-Dastjerdi , Mohamed Saber, Marwa M. Eid. "Monthly Solar Prediction Using Machine Learning: Diyala Governorate, Iraq as a Case Study." Journal of Journal of Artificial Intelligence and Metaheuristics, 5 no. 2 (2023): 41-46 (Doi : https://doi.org/10.54216/JAIM.050204) |
| Harvard | Noor Razzaq Abbas , Hussein Alkattan , Hamidreza Rabiei-Dastjerdi , Mohamed Saber, Marwa M. Eid. (2023). Monthly Solar Prediction Using Machine Learning: Diyala Governorate, Iraq as a Case Study. Journal of Journal of Artificial Intelligence and Metaheuristics, 5 ( 2 ), 41-46 (Doi : https://doi.org/10.54216/JAIM.050204) |
| Vancouver | Noor Razzaq Abbas , Hussein Alkattan , Hamidreza Rabiei-Dastjerdi , Mohamed Saber, Marwa M. Eid. Monthly Solar Prediction Using Machine Learning: Diyala Governorate, Iraq as a Case Study. Journal of Journal of Artificial Intelligence and Metaheuristics, (2023); 5 ( 2 ): 41-46 (Doi : https://doi.org/10.54216/JAIM.050204) |
| IEEE | Noor Razzaq Abbas, Hussein Alkattan, Hamidreza Rabiei-Dastjerdi, Mohamed Saber, Marwa M. Eid, Monthly Solar Prediction Using Machine Learning: Diyala Governorate, Iraq as a Case Study, Journal of Journal of Artificial Intelligence and Metaheuristics, Vol. 5 , No. 2 , (2023) : 41-46 (Doi : https://doi.org/10.54216/JAIM.050204) |