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Predicting Solar Power Generation based on the Combination of Meteorological Parameters in Iran: Neural Networks Approach

Document Type : Research Note

Authors

1 Associate Professor, Department of Industrial Engineering & Management Systems, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.

2 Master of Industrial Engineering, Department of Economics, Kharazmi University, Tehran, Iran.

3 Associate Professor, Department of Economics, Kharazmi University, Tehran, Iran.

10.30501/jree.2023.363386.1461

Abstract

Clean solar energy is one of the best sources of energy. The large number of sunny days in Iran makes it ideal for solar power plants to generate electricity. This paper presents a short-term forecasting approach based on artificial neural networks (ANNs) for selected solar power plants in Iran and ranks the input variables of the neural network according to their importance. Two solar power plants in Hamadan province (Amirkabir and Khalij-Fars) were selected for the project. The output of solar power plants is dependent on weather conditions. Solar radiation on the horizontal plane, air temperature, air pressure, day length, number of sunny hours, cloudiness, and airborne dust particles are considered input variables in this study to predict solar power plant output. Forecasting model selection is based on considering zero and nonzero quantities of target variables. The results show that solar production forecasting based on meteorological parameters in the Khalij-Fars is more accurate than Amirkabir. The global solar radiation, air temperature, number of sunny hours, day length, airborne dust particles, cloudiness, air pressure, and dummy variables[1] are the order of the most important inputs to solar power generation. Results show simultaneous influences of radiation and temperature on solar power plant production.

Keywords

Main Subjects

References
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Journal of Renewable Energy and Environment

Articles in Press, Accepted Manuscript
Available Online from 08 February 2023
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  • Receive Date: 07 October 2022
  • Revise Date: 03 January 2023
  • Accept Date: 17 January 2023
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