Open Access

Advancements and Challenges in Solar Radiation Prediction: A Review of Machine Learning Approaches

K. C. Jayasankar, jayasankarkc3002.sse@saveetha.com
Department of Electrical and Electronics Engineering, Saveetha School of Engineering, SIMATS, Saveetha University, Chennai, TN, India G. Anandhakumar, Department of Electrical and Electronics Engineering, Saveetha School of Engineering, SIMATS, Saveetha University, Chennai, TN, India A. Kalaimurugan Department of Electrical and Electronics Engineering, Agni College of Technology, Chennai, TN, India

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Abstract

Solar irradiance prediction holds paramount importance in optimizing the efficacy and dependability of solar power systems. This evaluative manuscript delves into an array of machine learning methodologies applied in solar irradiance prediction, encompassing conventional methodologies. It further delves into the ramifications of meteorological data, geographical and temporal parameters, data preprocessing, feature curation methodologies, amalgamated learning methodologies, and composite models. Moreover, the manuscript investigates the latent advantages of amalgamating orbital visuals and terrestrial sensors to augment prognostic capacities. Ultimately, the review underscores the exigency for broader datasets, refined feature curation methodologies, and composite models to attain heightened precision and resilience.

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Reference

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Alam, M., Abido, M., El-Amin, I., Fault Current Limiters in Power Systems: A Comprehensive Review, Energies 11(5), 1025 (2018).

https://doi.org/10.3390/en11051025

Alam, M. S., Al-Ismail, F. S., Salem, A., Abido, M. A., High-Level Penetration of Renewable Energy Sources Into Grid Utility: Challenges and Solutions, IEEE Access 8, 190277–190299 (2020).

https://doi.org/10.1109/ACCESS.2020.3031481

Arora, I., Gambhir, J., Kaur, T., Data Normalisation-Based Solar Irradiance Forecasting Using Artificial Neural Networks, Arab. J. Sci. Eng. 46(2), 1333–1343 (2021).

https://doi.org/10.1007/s13369-020-05140-y

Bhatnagar, M., Yadav, A., Swetapadma, A., Enhancing the resiliency of transmission lines using extreme gradient boosting against faults, Electr. Power Syst. Res. 207, 107850 (2022).

https://doi.org/10.1016/j.epsr.2022.107850

Bounoua, Z., Ouazzani Chahidi, L., Mechaqrane, A., Estimation of daily global solar radiation using empirical and machine-learning methods: A case study of five Moroccan locations, Sustain. Mater. Technol. 28, e00261 (2021).

https://doi.org/10.1016/j.susmat.2021.e00261

Chaibi, M., Benghoulam, E. M., Tarik, L., Berrada, M., Hmaidi, A. El, An Interpretable Machine Learning Model for Daily Global Solar Radiation Prediction, Energies 14(21), 7367 (2021).

https://doi.org/10.3390/en14217367

Dong, H., Yang, L., Zhang, S., Li, Y., An Improved Prediction Approach on Solar Irradiance of Photovoltaic Power Station, TELKOMNIKA Indones, J. Electr. Eng.,

https://doi.org/10.11591/telkomnika.v12i3.4017

Dong, N., Chang, J.-F., Wu, A.-G., Gao, Z.-K., A novel convolutional neural network framework based solar irradiance prediction method, Int. J. Electr. Power Energy Syst. 114, 105411 (2020).

https://doi.org/10.1016/j.ijepes.2019.105411

Guo, H., Zhuang, X., Chen, P., Alajlan, N., Rabczuk, T., Analysis of three-dimensional potential problems in non-homogeneous media with physics-informed deep collocation method using material transfer learning and sensitivity analysis, Eng. Comput. 38(6), 5423–5444 (2022a).

https://doi.org/10.1007/s00366-022-01633-6

Guo, H., Zhuang, X., Chen, P., Alajlan, N., Rabczuk, T., Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media, Eng. Comput. 38(6), 5173–5198 (2022b).

https://doi.org/10.1007/s00366-021-01586-2

Prasad, R., Ali, M., Kwan, P., Khan, H., Designing a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest model to forecast monthly solar radiation, Appl. Energy 236, 778–792 (2019).

https://doi.org/10.1016/j.apenergy.2018.12.034

Rabbi, K. M., Nandi, I., Saleh, A. S., Faisal, F., Mojumder, S., Prediction of solar irradiation in Bangladesh using artificial neural network (ANN) and data mapping using GIS technology, In: 2016 4th International Conference on the Development in the in Renewable Energy Technology (ICDRET). IEEE, pp 1–6, (2016).

https://doi.org/10.1109/ICDRET.2016.7421482

Ren, Y., Suganthan, P. N., Srikanth, N., Ensemble methods for wind and solar power forecasting—A state-of-the-art review, Renew. Sustain. Energy Rev. 50, 82–91 (2015).

https://doi.org/10.1016/j.rser.2015.04.081

Sharifi, S. S., Rezaverdinejad, V., Nourani, V., Estimation of daily global solar radiation using wavelet regression, ANN, GEP and empirical models: A comparative study of selected temperature-based approaches, J. Atmos. Solar-Terrestrial Phys. 149, 131–145 (2016).

https://doi.org/10.1016/j.jastp.2016.10.008