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A Novel Machine Learning Approach for Solar Radiation Estimation

Author

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  • Hasna Hissou

    (Faculty of Science, Science and Technology Research Structure, Chouaïb Doukkali University, El Jadida 24000, Morocco)

  • Said Benkirane

    (Technology Higher School Essaouira, Cadi Ayyad University, Essaouira 44000, Morocco)

  • Azidine Guezzaz

    (Technology Higher School Essaouira, Cadi Ayyad University, Essaouira 44000, Morocco)

  • Mourade Azrour

    (IDMS Team, Faculty of Sciences and Techniques, Moulay Ismail University of Meknès, Errachidia 25000, Morocco)

  • Abderrahim Beni-Hssane

    (Faculty of Science, Science and Technology Research Structure, Chouaïb Doukkali University, El Jadida 24000, Morocco)

Abstract

Solar irradiation (Rs) is the electromagnetic radiation energy emitted by the Sun. It plays a crucial role in sustaining life on Earth by providing light, heat, and energy. Furthermore, it serves as a key driver of Earth’s climate and weather systems, influencing the distribution of heat across the planet, shaping global air and ocean currents, and determining weather patterns. Variations in Rs levels have significant implications for climate change and long-term climate trends. Moreover, Rs represents an abundant and renewable energy resource, offering a clean and sustainable alternative to fossil fuels. By harnessing solar energy, we can actively reduce greenhouse gas emissions. However, the utilization of Rs comes with its own challenges that must be addressed. One problem is its variability, which makes it difficult to predict and plan for consistent solar energy generation. Its intermittent nature also poses difficulties in meeting continuous energy demand unless appropriate energy storage or backup systems are in place. Integrating large-scale solar energy systems into existing power grids can present technical challenges. Rs levels are influenced by various factors; understanding these factors is crucial for various applications, such as renewable energy planning, climate modeling, and environmental studies. Overcoming the associated challenges requires advancements in technology and innovative solutions. Measuring and harnessing Rs for various applications can be achieved using various devices; however, the expense and scarcity of measuring equipment pose challenges in accurately assessing and monitoring Rs levels. In order to address this, alternative methods have been developed with which to estimate Rs, including artificial intelligence and machine learning (ML) models, like neural networks, kernel algorithms, tree-based models, and ensemble methods. To demonstrate the impact of feature selection methods on Rs predictions, we propose a Multivariate Time Series (MVTS) model using Recursive Feature Elimination (RFE) with a decision tree (DT), Pearson correlation (Pr), logistic regression (LR), Gradient Boosting Models (GBM), and a random forest (RF). Our article introduces a novel framework that integrates various models and incorporates overlooked factors. This framework offers a more comprehensive understanding of Recursive Feature Elimination and its integrations with different models in multivariate solar radiation forecasting. Our research delves into unexplored aspects and challenges existing theories related to solar radiation forecasting. Our results show reliable predictions based on essential criteria. The feature ranking may vary depending on the model used, with the RF Regressor algorithm selecting features such as maximum temperature, minimum temperature, precipitation, wind speed, and relative humidity for specific months. The DT algorithm may yield a slightly different set of selected features. Despite the variations, all of the models exhibit impressive performance, with the LR model demonstrating outstanding performance with low RMSE (0.003) and the highest R2 score (0.002). The other models also show promising results, with RMSE scores ranging from 0.006 to 0.007 and a consistent R2 score of 0.999.

Suggested Citation

  • Hasna Hissou & Said Benkirane & Azidine Guezzaz & Mourade Azrour & Abderrahim Beni-Hssane, 2023. "A Novel Machine Learning Approach for Solar Radiation Estimation," Sustainability, MDPI, vol. 15(13), pages 1-21, July.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10609-:d:1187503
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    1. Marzo, A. & Trigo-Gonzalez, M. & Alonso-Montesinos, J. & Martínez-Durbán, M. & López, G. & Ferrada, P. & Fuentealba, E. & Cortés, M. & Batlles, F.J., 2017. "Daily global solar radiation estimation in desert areas using daily extreme temperatures and extraterrestrial radiation," Renewable Energy, Elsevier, vol. 113(C), pages 303-311.
    2. Pang, Zhihong & Niu, Fuxin & O’Neill, Zheng, 2020. "Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons," Renewable Energy, Elsevier, vol. 156(C), pages 279-289.
    3. 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.
    4. Chen, Ji-Long & Liu, Hong-Bin & Wu, Wei & Xie, De-Ti, 2011. "Estimation of monthly solar radiation from measured temperatures using support vector machines – A case study," Renewable Energy, Elsevier, vol. 36(1), pages 413-420.
    5. Benali, L. & Notton, G. & Fouilloy, A. & Voyant, C. & Dizene, R., 2019. "Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components," Renewable Energy, Elsevier, vol. 132(C), pages 871-884.
    6. Mohamed Chaibi & EL Mahjoub Benghoulam & Lhoussaine Tarik & Mohamed Berrada & Abdellah El Hmaidi, 2021. "An Interpretable Machine Learning Model for Daily Global Solar Radiation Prediction," Energies, MDPI, vol. 14(21), pages 1-19, November.
    7. Meenal, R. & Selvakumar, A. Immanuel, 2018. "Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters," Renewable Energy, Elsevier, vol. 121(C), pages 324-343.
    8. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2022. "Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms," Applied Energy, Elsevier, vol. 316(C).
    9. Youngsung Kwon & Alexis Kwasinski & Andres Kwasinski, 2019. "Solar Irradiance Forecast Using Naïve Bayes Classifier Based on Publicly Available Weather Forecasting Variables," Energies, MDPI, vol. 12(8), pages 1-13, April.
    10. Abdel-Rahman Hedar & Majid Almaraashi & Alaa E. Abdel-Hakim & Mahmoud Abdulrahim, 2021. "Hybrid Machine Learning for Solar Radiation Prediction in Reduced Feature Spaces," Energies, MDPI, vol. 14(23), pages 1-29, November.
    11. Hassan, Muhammed A. & Khalil, A. & Kaseb, S. & Kassem, M.A., 2017. "Potential of four different machine-learning algorithms in modeling daily global solar radiation," Renewable Energy, Elsevier, vol. 111(C), pages 52-62.
    12. Mohanty, Sthitapragyan & Patra, Prashanta Kumar & Sahoo, Sudhansu Sekhar, 2016. "Prediction and application of solar radiation with soft computing over traditional and conventional approach – A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 778-796.
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    3. Oubah Isman Okieh & Serhat Seker & Seckin Gokce & Martin Dennenmoser, 2024. "An Enhanced Forecasting Method of Daily Solar Irradiance in Southwestern France: A Hybrid Nonlinear Autoregressive with Exogenous Inputs with Long Short-Term Memory Approach," Energies, MDPI, vol. 17(16), pages 1-21, August.
    4. Jerome G. Gacu & Junrey D. Garcia & Eddie G. Fetalvero & Merian P. Catajay-Mani & Cris Edward F. Monjardin, 2023. "Suitability Analysis Using GIS-Based Analytic Hierarchy Process (AHP) for Solar Power Exploration," Energies, MDPI, vol. 16(18), pages 1-28, September.
    5. Zhuoyuan Lyu & Ying Shen & Yu Zhao & Tao Hu, 2023. "Solar Radiation Prediction Based on Conformer-GLaplace-SDAR Model," Sustainability, MDPI, vol. 15(20), pages 1-18, October.
    6. Yang Liu & Tianxing Yang & Liwei Tian & Bincheng Huang & Jiaming Yang & Zihan Zeng, 2024. "Ada-XG-CatBoost: A Combined Forecasting Model for Gross Ecosystem Product (GEP) Prediction," Sustainability, MDPI, vol. 16(16), pages 1-19, August.

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