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Comparative Analysis of Supervised Learning Techniques for Forecasting PV Current in South Africa

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  • Ely Ondo Ekogha

    (Department of Computer System Engineering, Tshwane University of Technology, Pretoria 0001, South Africa)

  • Pius A. Owolawi

    (Department of Computer System Engineering, Tshwane University of Technology, Pretoria 0001, South Africa)

Abstract

The fluctuations in solar irradiance and temperature throughout the year require an accurate methodology for forecasting the generated current of a PV system based on its specifications. The optimal technique must effectively manage rapid weather fluctuations while maintaining high accuracy in forecasting the performance of a PV panel. This work presents a comparative examination of supervised learning algorithms optimized with particle swarm optimization for estimating photovoltaic output current. The empirical formula’s measured currents are compared with outputs from various neural networks techniques, including feedforward neural networks (FFNNs), the general regression network known as GRNN, cascade forward neural networks also known as CFNNs, and adaptive fuzzy inference systems known as ANFISs, all optimized for enhanced accuracy using the particle swarm optimization (PSO) method. The ground data utilized for these models comprises hourly irradiations and temperatures from 2023, sourced from several places in South Africa. The accuracy levels indicated by statistical error margins from the root mean square error (RMSE), mean bias error (MBE), and mean absolute percentage error (MAPE) imply a universal enhancement in the algorithms’ precision upon optimization.

Suggested Citation

  • Ely Ondo Ekogha & Pius A. Owolawi, 2024. "Comparative Analysis of Supervised Learning Techniques for Forecasting PV Current in South Africa," Forecasting, MDPI, vol. 7(1), pages 1-20, December.
    • Handle: RePEc:gam:jforec:v:7:y:2024:i:1:p:1-:d:1553856
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    References listed on IDEAS

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    3. Theocharides, Spyros & Makrides, George & Livera, Andreas & Theristis, Marios & Kaimakis, Paris & Georghiou, George E., 2020. "Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing," Applied Energy, Elsevier, vol. 268(C).
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