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Predicting Solar Irradiance at Several Time Horizons Using Machine Learning Algorithms

Author

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  • Chibuzor N. Obiora

    (Department of Electrical and Electronic Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg 2092, South Africa)

  • Ali N. Hasan

    (Department of Electrical and Electronic Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg 2092, South Africa)

  • Ahmed Ali

    (Department of Electrical and Electronic Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg 2092, South Africa)

Abstract

Photovoltaic (PV) panels need to be exposed to sufficient solar radiation to produce the desired amount of electrical power. However, due to the stochastic nature of solar irradiance, smooth solar energy harvesting for power generation is challenging. Most of the available literature uses machine learning models trained with data gathered over a single time horizon from a location to forecast solar radiation. This study uses eight machine learning models trained with data gathered at various time horizons over two years in Limpopo, South Africa, to forecast solar irradiance. The goal was to study how the time intervals for forecasting the patterns of solar radiation affect the performance of the models in addition to determining their accuracy. The results of the experiments generally demonstrate that the models’ accuracy decreases as the prediction horizons get longer. Predictions were made at 5, 10, 15, 30, and 60 min intervals. In general, the deep learning models outperformed the conventional machine learning models. The Convolutional Long Short-Term Memory (ConvLSTM) model achieved the best Root Mean Square Error (RMSE) of 7.43 at a 5 min interval. The Multilayer Perceptron (MLP) model, however, outperformed other models in most of the prediction intervals.

Suggested Citation

  • Chibuzor N. Obiora & Ali N. Hasan & Ahmed Ali, 2023. "Predicting Solar Irradiance at Several Time Horizons Using Machine Learning Algorithms," Sustainability, MDPI, vol. 15(11), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8927-:d:1161597
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    References listed on IDEAS

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    1. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    2. Li, Jiaming & Ward, John K. & Tong, Jingnan & Collins, Lyle & Platt, Glenn, 2016. "Machine learning for solar irradiance forecasting of photovoltaic system," Renewable Energy, Elsevier, vol. 90(C), pages 542-553.
    3. Sheng Li & Yi Jiang & Shuisong Ke & Ke Nie & Chao Wu, 2021. "Understanding the Effects of Influential Factors on Housing Prices by Combining Extreme Gradient Boosting and a Hedonic Price Model (XGBoost-HPM)," Land, MDPI, vol. 10(5), pages 1-15, May.
    4. Maciej Dzikuć & Arkadiusz Piwowar, 2022. "Economic Aspects of Low Carbon Development," Energies, MDPI, vol. 15(14), pages 1-3, July.
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    Cited by:

    1. Xiaoying Ren & Fei Zhang & Junshuai Yan & Yongqian Liu, 2024. "A Novel Convolutional Neural Net Architecture Based on Incorporating Meteorological Variable Inputs into Ultra-Short-Term Photovoltaic Power Forecasting," Sustainability, MDPI, vol. 16(7), pages 1-21, March.

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