Wavelet-Enhanced Machine Learning Models for Hourly Solar Irradiance Forecasting

Gbadebo İsmaila Olatona; Shuaib Adisa; Muyideen Olalekan Lawal; Saheed Adewinbi; Sherifdeen Mosebolatan Oyedokun

doi:10.34110/forecasting.1855955

Wavelet-Enhanced Machine Learning Models for Hourly Solar Irradiance Forecasting

Abstract

Forecasting solar irradiance accurately is critical for optimizing renewable energy systems. In this study CWT was applied to solar irradiance for time frequency features. The CWT features, meteorological data and lag solar irradiance data were used to train seven ML models. The models were trained with 70% and assessed with 30% of the dataset with five statistical metrics. Results showed that MLP consistently achieved the best predictive accuracy with 12.9494 MAE, 339.81 MSE, 18.43 RMSE, -1.86 MBE and 0.9945 R2, while RF and GBR also performed competitively. Also, kNN with 32.71 MAE, 2115 MSE, 45.99 RMSE, -5.03 MBE and 0.97 R2 exhibited the weakest performance. These results show that CWT coefficient statistical features allow model like MLP, RF and GBR to capture irradiance variability more effectively.

Keywords

References

Ağbulut, Ü., Gürel, A. E., & Biçen, Y. (2020). Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison. Renewable and Sustainable Energy Reviews, 135, 110114. https://doi.org/10.1016/j.rser.2020.110114
Almaghrabi, S., Rana, M., Hamilton, M., & Rahaman, M. S. (2022). Solar power time series forecasting utilising wavelet coefficients. Neurocomputing, 508, 182–207. https://doi.org/10.1016/j.neucom.2022.08.016
Alonso-Montesinos, J., & Batlles, F. (2015). Solar radiation forecasting in the short- and medium-term under all sky conditions. Energy, 83, 387–393. https://doi.org/10.1016/j.energy.2015.02.036
Amer, A. A., Ravana, S. D., & Habeeb, R. a. A. (2025). Effective k-nearest neighbor models for data classification enhancement. Journal of Big Data, 12(1). https://doi.org/10.1186/s40537-025-01137-2
Badaoui, H. E., Abdallaoui, A., & Chabaa, S. (2013). Using MLP neural networks for predicting global solar radiation. The International Journal of Engineering and Science, Volume 2(Issue12), 48–56. Benali, L., Notton, G., Fouilloy, A., Voyant, C., & Dizene, R. (2018). Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components. Renewable Energy, 132, 871–884. Caruana, R., & Niculescu-Mizil, A. (2006). An empirical comparison of supervised learning algorithms. 23rd International Conference on Machine Learning, 161–168. https://doi.org/10.1145/1143844.1143865 Chodakowska, E., Nazarko, J., Nazarko, Ł., & Rabayah, H. S. (2024). Solar Radiation Forecasting: A Systematic Meta-Review of Current Methods and Emerging Trends. Energies, 17(13), 3156. https://doi.org/10.3390/en17133156
El-Shahat, D., Tolba, A., Abouhawwash, M., & Abdel-Basset, M. (2024). Machine learning and deep learning models-based grid search cross validation for short-term solar irradiance forecasting. Journal of Big Data, 11(1). https://doi.org/10.1186/s40537-024-00991-w
Erbs, D., Klein, S., & Duffie, J. (1982). Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation. Solar Energy, 28(4), 293–302. https://doi.org/10.1016/0038-092x(82)90302-4
Esposito, E., Leanza, G., & Di Francia, G. (2024). Comparative analysis of Ground-Based solar irradiance measurements and Copernicus satellite observations. Energies, 17(7), 1579. https://doi.org/10.3390/en17071579

Falayi E. O, Adewole A. T., Adelaja A. D., Roy-Layinde T. O. (2020), “Wavelet spectrum analysis of Air temperature and Relative humidity in some selected stations in Nigeria”, Dutse Journal of Pure and Applied Sciences, vol. 6, no. 1, pp. 47-59.
Khanlari, A., Sözen, A., Afshari, F., Şirin, C., Tuncer, A. D., & Gungor, A. (2019a). Drying municipal sewage sludge with v-groove triple-pass and quadruple-pass solar air heaters along with testing of a solar absorber drying chamber. The Science of the Total Environment, 709, 136198. https://doi.org/10.1016/j.scitotenv.2019.136198
Khanlari, A., Sözen, A., Şirin, C., Tuncer, A. D., & Gungor, A. (2019b). Performance enhancement of a greenhouse dryer: Analysis of a cost-effective alternative solar air heater. Journal of Cleaner Production, 251, 119672. https://doi.org/10.1016/j.jclepro.2019.119672
Menze, B. H., Kelm, B. M., Masuch, R., Himmelreich, U., Bachert, P., Petrich, W., & Hamprecht, F. A. (2009). A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinformatics, 10(1). https://doi.org/10.1186/1471-2105-10-213
Mohammadi, K., Shamshirband, S., Anisi, M. H., Alam, K. A., & Petković, D. (2014). Support vector regression based prediction of global solar radiation on a horizontal surface. Energy Conversion and Management, 91, 433–441. https://doi.org/10.1016/j.enconman.2014.12.015
Orgill, J., & Hollands, K. (1977). Correlation equation for hourly diffuse radiation on a horizontal surface. Solar Energy, 19(4), 357–359. https://doi.org/10.1016/0038-092x(77)90006-8
Priya, K. S. (2021). Linear Regression Algorithm in Machine Learning through MATLAB. International Journal for Research in Applied Science and Engineering Technology, 9(12), 989–995. https://doi.org/10.22214/ijraset.2021.39410
Reindl, D., Beckman, W., & Duffie, J. (1990). Diffuse fraction correlations. Solar Energy, 45(1), 1–7. https://doi.org/10.1016/0038-092x(90)90060-p
Sananmuang, T., Mankong, K., & Chokeshaiusaha, K. (2024). Multilayer perceptron and support vector regression models for feline parturition date prediction. Heliyon, 10(6), e27992. https://doi.org/10.1016/j.heliyon.2024.e27992
Sarica, A., Cerasa, A., & Quattrone, A. (2017). Random Forest Algorithm for the Classification of neuroimaging data in Alzheimer’s Disease: a Systematic review. Frontiers in Aging Neuroscience, 9. https://doi.org/10.3389/fnagi.2017.00329
Singla, P., Duhan, M., & Saroha, S. (2021). An ensemble method to forecast 24-h ahead solar irradiance using wavelet decomposition and BiLSTM deep learning network. Earth Science Informatics, 15(1), 291–306. https://doi.org/10.1007/s12145-021-00723-1
Tandon, A., Awasthi, A., Pattnayak, K. C., Tandon, A., Choudhury, T., & Kotecha, K. (2025). Machine learning-driven solar irradiance prediction: advancing renewable energy in Rajasthan. Deleted Journal, 7(2). https://doi.org/10.1007/s42452-025-06490-8
Tasci, E., & Onan, A. (2017). K-En Yakın Komşu Algoritması Parametrelerinin Sınıflandırma Performansı Üzerine Etkisinin İncelenmesi.
Torrence, C., and Compo, G.P. (1998). A Practical Guide to Wavelet Analysis. Bulletin of the American Meteorological Society, 79: 61–78.
Uday kiran, (2018). Usage of neural networks in communication links with structural inverted vee antenna, International journal of engineering Research and applications (IJERA), vol.8, no.9, 2018, pp. 65-69.
Yin, S., & Liu, H. (2022). Wind power prediction based on outlier correction, ensemble reinforcement learning, and residual correction. Energy, 250, 123857. https://doi.org/10.1016/j.energy.2022.123857
Zafar, R., Vu, B. H., Husein, M., & Chung, I. (2021). Day-Ahead Solar Irradiance Forecasting Using Hybrid Recurrent Neural Network with Weather Classification for Power System Scheduling. Applied Sciences, 11(15), 6738. https://doi.org/10.3390/app11156738
Zhang, B., Ren, H., Huang, G., Cheng, Y., & Hu, C. (2019). Predicting blood pressure from physiological index data using the SVR algorithm. BMC Bioinformatics, 20(1). https://doi.org/10.1186/s12859-019-2667-y

Details

Primary Language

English

Subjects

Computational Statistics

Journal Section

Research Article

Authors

Gbadebo İsmaila Olatona
0000-0001-9415-6265
Nigeria

Shuaib Adisa ^*
0000-0001-9389-1874
Nigeria

Muyideen Olalekan Lawal
0000-0001-8705-0569
Nigeria

Saheed Adewinbi
0000-0002-1057-2462
Nigeria

Sherifdeen Mosebolatan Oyedokun
0000-0001-9013-5512
Nigeria

Publication Date

March 12, 2026

Submission Date

January 4, 2026

Acceptance Date

January 21, 2026

Published in Issue

Year 2026 Volume: 10 Number: 1

DOI

https://doi.org/10.34110/forecasting.1855955

IZ

https://izlik.org/JA68XU76DC

Cite

RIS / Bibtex

APA

Olatona, G. İ., Adisa, S., Lawal, M. O., Adewinbi, S., & Oyedokun, S. M. (2026). Wavelet-Enhanced Machine Learning Models for Hourly Solar Irradiance Forecasting. Turkish Journal of Forecasting, 10(1), 20-28. https://doi.org/10.34110/forecasting.1855955

AMA

1.Olatona Gİ, Adisa S, Lawal MO, Adewinbi S, Oyedokun SM. Wavelet-Enhanced Machine Learning Models for Hourly Solar Irradiance Forecasting. TJF. 2026;10(1):20-28. doi:10.34110/forecasting.1855955

Chicago

Olatona, Gbadebo İsmaila, Shuaib Adisa, Muyideen Olalekan Lawal, Saheed Adewinbi, and Sherifdeen Mosebolatan Oyedokun. 2026. “Wavelet-Enhanced Machine Learning Models for Hourly Solar Irradiance Forecasting”. Turkish Journal of Forecasting 10 (1): 20-28. https://doi.org/10.34110/forecasting.1855955.

EndNote

Olatona Gİ, Adisa S, Lawal MO, Adewinbi S, Oyedokun SM (March 1, 2026) Wavelet-Enhanced Machine Learning Models for Hourly Solar Irradiance Forecasting. Turkish Journal of Forecasting 10 1 20–28.

IEEE

[1]G. İ. Olatona, S. Adisa, M. O. Lawal, S. Adewinbi, and S. M. Oyedokun, “Wavelet-Enhanced Machine Learning Models for Hourly Solar Irradiance Forecasting”, TJF, vol. 10, no. 1, pp. 20–28, Mar. 2026, doi: 10.34110/forecasting.1855955.

ISNAD

Olatona, Gbadebo İsmaila - Adisa, Shuaib - Lawal, Muyideen Olalekan - Adewinbi, Saheed - Oyedokun, Sherifdeen Mosebolatan. “Wavelet-Enhanced Machine Learning Models for Hourly Solar Irradiance Forecasting”. Turkish Journal of Forecasting 10/1 (March 1, 2026): 20-28. https://doi.org/10.34110/forecasting.1855955.

JAMA

1.Olatona Gİ, Adisa S, Lawal MO, Adewinbi S, Oyedokun SM. Wavelet-Enhanced Machine Learning Models for Hourly Solar Irradiance Forecasting. TJF. 2026;10:20–28.

MLA

Olatona, Gbadebo İsmaila, et al. “Wavelet-Enhanced Machine Learning Models for Hourly Solar Irradiance Forecasting”. Turkish Journal of Forecasting, vol. 10, no. 1, Mar. 2026, pp. 20-28, doi:10.34110/forecasting.1855955.

Vancouver

1.Gbadebo İsmaila Olatona, Shuaib Adisa, Muyideen Olalekan Lawal, Saheed Adewinbi, Sherifdeen Mosebolatan Oyedokun. Wavelet-Enhanced Machine Learning Models for Hourly Solar Irradiance Forecasting. TJF. 2026 Mar. 1;10(1):20-8. doi:10.34110/forecasting.1855955