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Predictions of Surface Solar Radiation on Tilted Solar Panels using Machine Learning Models: A Case Study of Tainan City, Taiwan

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  • Chih-Chiang Wei

    (Department of Marine Environmental Informatics, National Taiwan Ocean University, No. 2, Beining Rd., Jhongjheng District, Keelung City 20224, Taiwan)

Abstract

In this paper, forecasting models were constructed to estimate surface solar radiation on an hourly basis and the solar irradiance received by solar panels at different tilt angles, to enhance the capability of photovoltaic systems by estimating the amount of electricity they generate, thereby improving the reliability of the power they supply. The study site was Tainan in southern Taiwan, which receives abundant sunlight because of its location at a latitude of approximately 23°. Four forecasting models of surface solar irradiance were constructed, using the multilayer perceptron (MLP), random forests (RF), k -nearest neighbors ( k NN), and linear regression (LR), algorithms, respectively. The forecast horizon ranged from 1 to 12 h. The findings are as follows: first, solar irradiance was effectively estimated when a combination of ground weather data and solar position data was applied. Second, the mean absolute error was higher in MLP than in RF and kNN, and LR had the worst predictive performance. Third, the observed total solar irradiance was 1.562 million w/m 2 per year when the solar-panel tilt angle was 0° (i.e., the non-tilted position) and peaked at 1.655 million w/m 2 per year when the angle was 20–22°. The level of the irradiance was almost the same when the solar-panel tilt angle was 0° as when the angle was 41°. In summary, the optimal solar-panel tilt angle in Tainan was 20–22°.

Suggested Citation

  • Chih-Chiang Wei, 2017. "Predictions of Surface Solar Radiation on Tilted Solar Panels using Machine Learning Models: A Case Study of Tainan City, Taiwan," Energies, MDPI, vol. 10(10), pages 1-26, October.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1660-:d:115856
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    Cited by:

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    11. 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.
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