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Nowcasting Hourly-Averaged Tilt Angles of Acceptance for Solar Collector Applications Using Machine Learning Models

Author

Listed:
  • Ronewa Collen Nemalili

    (Department of Physics, University of Venda, Thohoyandou 0950, South Africa)

  • Lordwell Jhamba

    (Department of Physics, University of Venda, Thohoyandou 0950, South Africa)

  • Joseph Kiprono Kirui

    (Department of Physics, University of Venda, Thohoyandou 0950, South Africa)

  • Caston Sigauke

    (Department of Physics, University of Venda, Thohoyandou 0950, South Africa
    Department of Mathematical and Computational Sciences, University of Venda, Thohoyandou 0950, South Africa)

Abstract

Challenges in utilising fossil fuels for generating energy call for the adoption of renewable energy sources. This study focuses on modelling and nowcasting optimal tilt angle(s) of solar energy harnessing using historical time series data collected from one of South Africa’s radiometric stations, USAid Venda station in Limpopo Province. In the study, we compared random forest (RF), K-nearest neighbours (KNN), and long short-term memory (LSTM) in nowcasting of optimum tilt angle. Gradient boosting (GB) is used as the benchmark model to compare the model’s predictive accuracy. The performance measures of mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE) and R 2 were used, and the results showed LSTM to have the best performance in nowcasting optimum tilt angle compared to other models, followed by the RF and GB, whereas KNN was the worst-performing model.

Suggested Citation

  • Ronewa Collen Nemalili & Lordwell Jhamba & Joseph Kiprono Kirui & Caston Sigauke, 2023. "Nowcasting Hourly-Averaged Tilt Angles of Acceptance for Solar Collector Applications Using Machine Learning Models," Energies, MDPI, vol. 16(2), pages 1-19, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:927-:d:1035452
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    References listed on IDEAS

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