Evaluation of Artificial Neural Networks with Satellite Data Inputs for Daily, Monthly, and Yearly Solar Irradiation Prediction for Pakistan

Faisal Nawab, Ag Sufiyan Abd Hamid, Ali Alwaeli, Muhammad Arif, Mohd Faizal Fauzan and Adnan Ibrahim
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Faisal Nawab: Solar Energy Research Institute, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
Ag Sufiyan Abd Hamid: Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Kota Kinabalu 88400, Malaysia
Ali Alwaeli: Engineering Department, American University of Iraq, Sulaimani, Kurdistan Region, Sulaimani 46001, Iraq
Muhammad Arif: Center for Advanced Studies in Energy, University of Engineering and Technology, Peshawar 25120, Pakistan
Mohd Faizal Fauzan: Solar Energy Research Institute, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
Adnan Ibrahim: Solar Energy Research Institute, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia

Sustainability, 2022, vol. 14, issue 13, 1-20

Abstract: Solar irradiation is the most critical parameter to consider when designing solar energy systems. The high cost and difficulty of measuring solar irradiation makes it impractical in every location. This study’s primary objective was to develop an artificial neural network (ANN) model for global horizontal irradiation (GHI) prediction using satellite data inputs. Three types of ANN, namely, the feed forward neural network (FFNN), cascaded forward neural network (CFNN), and Elman neural network (EMNN), were tested. The findings revealed that altitude, relative humidity, and satellite GHI are the most effective parameters, as they are present in all the best-performing models. The best model for daily GHI prediction was FFNN, which decreased daily MAPE, RMSE, and MBE by 25.4%, 0.11 kWh/ m 2 / d , and 0.01 kWh/ m 2 / d . The FFNN daily MAPE, RMSE, and MBE values were 7.83%, 0.49 kWh/ m 2 / d , and 0.01 kWh/m 2 /d. The EMNN performed best for monthly and annual prediction, reducing monthly MAPE, RMSE, and MBE by 50.62%, 0.13 kWh/ m 2 / d , and 0.13 kWh/ m 2 / d , while the reduction for yearly was 91.6%, 0.11 kWh/ m 2 / d , 0.2 kWh/ m 2 / d . The EMNN monthly MAPE, RMSE, and MBE values were 3.36%, 0.16 kWh/ m 2 / d , and 0.16 kWh/ m 2 / d , while the yearly values were 0.47%, 0.18 kWh/ m 2 / d , and 0.004 kWh/ m 2 / d .

Keywords: solar radiation; artificial neural network; prediction; satellite inputs; Pakistan (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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