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Neural Network-Based Estimation of Solar Radiation Level

Author

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  • Joshua Kasirye

    (Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia)

  • Lynnate Jane Nazziwa

    (Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia)

  • Herman Wahid

    (Process Tomography and Instrumentation Research Group, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia)

Abstract

This paper presents the design and implementation of an Artificial Neural Network (ANN) model to estimate solar radiation using meteorological data for a four seasoned country, specifically in Sydney, Australia. The model aims to provide a cost-effective alternative to direct measurement by leveraging available data such as temperature, humidity, wind speed, sea level pressure and rainfall. The model’s performance is evaluated using two methods: a GUI-based neural network toolbox and custom MATLAB codes. Performance metrics, including Mean Squared Error (MSE) and the correlation coefficient (R), were assessed to identify the most effective model. The results show that ANN models can accurately predict solar radiation levels, offering important insights for adaptive solar energy systems. The study concludes by comparing the two approaches, emphasizing their respective strengths and limitations. This research highlights the potential of utilizing ANNs with easily accessible meteorological data to improve the efficiency of solar energy harvesting systems.

Suggested Citation

  • Joshua Kasirye & Lynnate Jane Nazziwa & Herman Wahid, 2024. "Neural Network-Based Estimation of Solar Radiation Level," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 11(12), pages 666-676, December.
    • Handle: RePEc:bjc:journl:v:11:y:2024:i:12:p:666-676
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