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Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation

Author

Listed:
  • Olubayo M. Babatunde

    (Department of Electrical Engineering, French South African Institute of Technology (F’SATI), Tshwane University of Technology, Pretoria 0183, South Africa)

  • Josiah L. Munda

    (Department of Electrical Engineering, French South African Institute of Technology (F’SATI), Tshwane University of Technology, Pretoria 0183, South Africa)

  • Yskandar Hamam

    (Department of Electrical Engineering, French South African Institute of Technology (F’SATI), Tshwane University of Technology, Pretoria 0183, South Africa
    École Supérieure d’Ingénieurs en Électrotechnique et Électronique, Cité Descartes, 2 Boulevard Blaise Pascal, Noisy-le-Grand, 93160 Paris, France)

Abstract

The use of solar powered systems is gradually getting more attention due to technological advances as well as cost effectiveness. Thus, solar powered systems like photovoltaic, concentrated solar power, concentrator photovoltaics, as well as hydrogen production systems are now commercially available for electricity generation. A major input to these systems is solar radiation data which is either partially available or not available in many remote communities. Predictive models can be used in estimating the amount and pattern of solar radiation in any location. This paper presents the use of evolutionary algorithm in improving the generalization capabilities and efficiency of multilayer feed-forward artificial neural network for the prediction of solar radiation using meteorological parameters as input. Meteorological parameters which included monthly average daily of: sunshine hour, solar radiation, maximum temperature and minimum temperature were used in the evaluation. Results show that the proposed model returned a RMSE of 1.1967, NSE of 0.8137 and R 2 of 0.8254.

Suggested Citation

  • Olubayo M. Babatunde & Josiah L. Munda & Yskandar Hamam, 2020. "Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation," Energies, MDPI, vol. 13(10), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2488-:d:358370
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    References listed on IDEAS

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    2. Domenico Palladino & Iole Nardi & Cinzia Buratti, 2020. "Artificial Neural Network for the Thermal Comfort Index Prediction: Development of a New Simplified Algorithm," Energies, MDPI, vol. 13(17), pages 1-27, September.

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