{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T05:25:50Z","timestamp":1775193950149,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T00:00:00Z","timestamp":1741824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"DST-CSIR National e-Science Postgraduate Teaching and Training Platform (NEPTTP)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>In today\u2019s world, where sustainable energy is essential for the planet\u2019s survival, accurate solar energy forecasting is crucial. This study focused on predicting short-term Global Horizontal Irradiance (GHI) using minute-averaged data from the Southern African Universities Radiometric Network (SAURAN) at the Univen Radiometric Station in South Africa. Various techniques were evaluated for their predictive accuracy, including Recurrent Neural Networks (RNNs), Support Vector Regression (SVR), Gradient Boosting (GB), Random Forest (RF), Stacking Ensemble, and Double Nested Stacking (DNS). The results indicated that RNN performed the best in terms of mean absolute error (MAE) and root mean squared error (RMSE) among the machine learning models. However, Stacking Ensemble with XGBoost as the meta-model outperformed all individual models, improving accuracy by 67.06% in MAE and 22.28% in RMSE. DNS further enhanced accuracy, achieving a 93.05% reduction in MAE and an 88.54% reduction in RMSE compared to the best machine learning model, as well as a 78.89% decrease in MAE and an 85.27% decrease in RMSE compared to the best single stacking model. Furthermore, experimenting with the order of the DNS meta-model revealed that using RF as the first-level meta-model followed by XGBoost yielded the highest accuracy, showing a 47.39% decrease in MAE and a 61.35% decrease in RMSE compared to DNS with RF at both levels. These findings underscore advanced stacking techniques\u2019 potential to improve GHI forecasting significantly.<\/jats:p>","DOI":"10.3390\/computation13030072","type":"journal-article","created":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T06:53:00Z","timestamp":1741848780000},"page":"72","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Short-Term Predictions of Global Horizontal Irradiance Using Recurrent Neural Networks, Support Vector Regression, Gradient Boosting Random Forest and Advanced Stacking Ensemble Approaches"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4015-9433","authenticated-orcid":false,"given":"Fhulufhelo Walter","family":"Mugware","sequence":"first","affiliation":[{"name":"Department of Mathematical and Computational Sciences, University of Venda, Private Bag X5050, Thohoyandou 0950, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3169-0601","authenticated-orcid":false,"given":"Thakhani","family":"Ravele","sequence":"additional","affiliation":[{"name":"Department of Mathematical and Computational Sciences, University of Venda, Private Bag X5050, Thohoyandou 0950, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7406-5291","authenticated-orcid":false,"given":"Caston","family":"Sigauke","sequence":"additional","affiliation":[{"name":"Department of Mathematical and Computational Sciences, University of Venda, Private Bag X5050, Thohoyandou 0950, South Africa"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,13]]},"reference":[{"key":"ref_1","first-page":"19","article-title":"Recent advances in biomass conversion technologies","volume":"6","author":"Demirbas","year":"2000","journal-title":"Energy Educ. Sci. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1002\/asia.201000797","article-title":"The legacy of fossil fuels","volume":"6","author":"Armaroli","year":"2011","journal-title":"Chem. Asian J."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Gb\u00e9mou, S., Eynard, J., Thil, S., Guillot, E., and Grieu, S. (2021). A comparative study of machine learning-based methods for global horizontal irradiance forecasting. Energies, 14.","DOI":"10.3390\/en14113192"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"101","DOI":"10.33223\/epj\/168115","article-title":"A hybrid deep learning framework for modeling the short term global horizontal irradiance prediction of a solar power plant in India","volume":"26","author":"Rajaprasad","year":"2023","journal-title":"Polityka-Energetyczna-Energy Policy J."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yamani, A.Z., and Alyami, S.N. (2021, January 8\u201310). Investigating Hourly Global Horizontal Irradiance Forecasting Using Long Short-Term Memory. Proceedings of the 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Brisbane, Australia.","DOI":"10.1109\/CSDE53843.2021.9718423"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1016\/j.renene.2018.08.044","article-title":"A Fouilloy, Cyril Voyant, and Rabah Dizene. Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components","volume":"132","author":"Benali","year":"2019","journal-title":"Renew. Energy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"101312","DOI":"10.1016\/j.segan.2024.101312","article-title":"Solar Radiation Forecasting using Gradient Boosting based Ensemble Learning Model for Various Climatic Zones","volume":"38","author":"Krishnan","year":"2024","journal-title":"Sustain. Energy Grids Netw."},{"key":"ref_8","first-page":"364","article-title":"A Novel Two Layer Stacking Ensemble for Improving Solar Irradiance Forecasting","volume":"10","author":"Nziyumva","year":"2023","journal-title":"Int. J. Eng. Res. Technol. (IJERT)"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1016\/j.egyr.2020.11.006","article-title":"Study on short-term photovoltaic power prediction model based on the Stacking Ensemble learning","volume":"6","author":"Guo","year":"2020","journal-title":"Energy Rep."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhou, B., Chen, X., Li, G., Gu, P., Huang, J., and Yang, B. (2023). Xgboost\u2014sfs and Double Nested Stacking Ensemble model for photovoltaic power forecasting under variable weather conditions. Sustainability, 15.","DOI":"10.3390\/su151713146"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mandic, D.P., and Chambers, J. (2001). Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability, John Wiley & Sons, Inc.","DOI":"10.1002\/047084535X"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Garcia-Pedrero, A., and Gomez-Gil, P. (2010, January 22\u201324). Time series forecasting using recurrent neural networks and wavelet reconstructed signals. Proceedings of the 2010 20th International Conference on Electronics Communications and Computers (CONIELECOMP), Cholula, Puebla, Mexico.","DOI":"10.1109\/CONIELECOMP.2010.5440775"},{"key":"ref_13","unstructured":"Vapnik, V.N. (1998). Statistical Learning Theory, Wiley."},{"key":"ref_14","first-page":"156","article-title":"Support vector regression machines","volume":"9","author":"Drucker","year":"1996","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1471","DOI":"10.1007\/s00704-019-02905-w","article-title":"Long-term monthly average temperature forecasting in some climate types of Iran, using the models SARIMA, SVR, and SVR-FA","volume":"138","author":"Aghelpour","year":"2019","journal-title":"Theor. Appl. Climatol."},{"key":"ref_16","unstructured":"Breiman, L., and Ihaka, R. (1984). Nonlinear Discriminant Analysis via Scaling and ACE, Department of Statistics, University of California Davis One Shields Avenue. Available online: https:\/\/www.stat.berkeley.edu\/~breiman\/nldiscanace.pdf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lahouar, A., and Slama, J.B.H. (2015, January 24\u201326). Random forests model for one day ahead load forecasting. Proceedings of the IREC2015 the Sixth International Renewable Energy Congress, Sousse, Tunisia.","DOI":"10.1109\/IREC.2015.7110975"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Nemalili, R.C., Jhamba, L., Kirui, J.K., and Sigauke, C. (2023). Nowcasting Hourly-Averaged Tilt Angles of Acceptance for Solar Collector Applications Using Machine Learning Models. Energies, 16.","DOI":"10.3390\/en16020927"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8318","DOI":"10.1007\/s10489-022-03958-7","article-title":"Deep neural networks for the quantile estimation of regional renewable energy production","volume":"53","author":"Aler","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","article-title":"Stacked generalization","volume":"5","author":"Wolpert","year":"1992","journal-title":"Neural Netw."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"van der Laan, M.J., Polley, E.C., and Hubbard, A.E. (2007). Super Learner. Stat. Appl. Genet. Mol. Biol., 6.","DOI":"10.2202\/1544-6115.1309"},{"key":"ref_24","unstructured":"Khandelwal, Y. (2021). Ensemble stacking for machine learning and deep learning. Anal. Vidhya, 9, Available online: https:\/\/www.analyticsvidhya.com\/blog\/2021\/08\/ensemble-stacking-for-machine-learning-and-deep-learning\/."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Divina, F., Gilson, A., Gom\u00e9z-Vela, F., Torres, M.G., and Torres, J.F. (2018). Stacking Ensemble learning for short-term electricity consumption forecasting. Energies, 11.","DOI":"10.3390\/en11040949"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Joy, T.T., Rana, S., Gupta, S., and Venkatesh, S. (2016, January 4\u20138). Hyperparameter tuning for big data using Bayesian optimisation. Proceedings of the 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico.","DOI":"10.1109\/ICPR.2016.7900023"},{"key":"ref_27","first-page":"937","article-title":"Bayesian optimization with inequality constraints","volume":"2014","author":"Gardner","year":"2014","journal-title":"ICML"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1007\/s10661-017-6025-0","article-title":"Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology","volume":"189","author":"Fox","year":"2017","journal-title":"Environ. Monit. Assess."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","article-title":"Variable selection using random forests","volume":"31","author":"Genuer","year":"2010","journal-title":"Pattern Recognit. 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