Iran has an average of 5.5 KWh per square meter solar radiation and 300 sunny days per year on 90% of the land. Regarding this amount of solar radiation and the necessity for solar potential zoning for better efficiencies, drawing solar potential maps is essential. In this study, the monthly data of 39 synoptic of Iran meteorological stations over years (1991-2000) has been used as the input data to the MATLAB software and artificial neural network (ANN). In the ANN, a multi-layer feed forward model is used. After applying the input data to the network with desired architecture, in output layer the solar radiation is predicted. The solar radiation anticipated by ANN is highly in accordance with meteorological data so that the final correlation coefficient is 0.96, depicting the great accuracy of the data derived from the software. By selecting the predicted data of ANN as input to ArcGIS software, the annual solar potential map of Iran is obtained.
Ameri, M., & Hadipoor, M. (2023). Evaluation of Artificial Neural Network Performance to Predict Daily Solar Radiation in Iran. Energy Engineering and Management, 4(3), 38-47.
MLA
Mehran Ameri; Mohamad Hadipoor. "Evaluation of Artificial Neural Network Performance to Predict Daily Solar Radiation in Iran", Energy Engineering and Management, 4, 3, 2023, 38-47.
HARVARD
Ameri, M., Hadipoor, M. (2023). 'Evaluation of Artificial Neural Network Performance to Predict Daily Solar Radiation in Iran', Energy Engineering and Management, 4(3), pp. 38-47.
VANCOUVER
Ameri, M., Hadipoor, M. Evaluation of Artificial Neural Network Performance to Predict Daily Solar Radiation in Iran. Energy Engineering and Management, 2023; 4(3): 38-47.
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