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Therefore, in order to produce energy at our end, we must use renewable energy resources like solar energy. To anticipate the generation of solar photovoltaic energy, this paper proposed the Stacked Ensemble framework for Solar Photovoltaic Energy Generation Prediction and Alerting System (SE2GPA). The fundamental models used in this proposed stack-based ensemble learning approach include Gradient Boosting, Extreme Gradient Boosting, LightGBM, and CatBoost. To provide even more precise results, this paper used Cat Boosting as the meta-model and result shows the better performance by reducing error of energy prediction compared to state-of-art papers. The proposed model achieved an RMSE of 2.02 and an R\u00b2 of 0.68, outperforming existing state-of-the-art models. Furthermore, this paper employed an alerting system to alert the user if the efficiency of the solar photovoltaic system drops significantly below some threshold value. The model was tested on a single dataset, which may restrict its generalizability. Furthermore, expanding the framework to multiple datasets, integrating real-time streaming data, and enhancing the alerting mechanism with predictive maintenance features.<\/jats:p>","DOI":"10.1007\/s43926-025-00249-8","type":"journal-article","created":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:23:04Z","timestamp":1765498984000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SE2GPA stacked ensemble framework for solar photovoltaic energy generation prediction and alerting system"],"prefix":"10.1007","volume":"5","author":[{"given":"Madhavi B.","family":"Desai","sequence":"first","affiliation":[]},{"given":"Dhaval J.","family":"Rana","sequence":"additional","affiliation":[]},{"given":"Rushil","family":"patel","sequence":"additional","affiliation":[]},{"given":"Urvi","family":"Shukharamwala","sequence":"additional","affiliation":[]},{"given":"Kalpesh","family":"Popat","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,12,12]]},"reference":[{"key":"249_CR1","doi-asserted-by":"publisher","DOI":"10.3390\/machines9120328","author":"A Abubakar","year":"2021","unstructured":"Abubakar A, Almeida CFM, Gemignani M. Review of artificial intelligence-based failure detection and diagnosis methods for solar photovoltaic systems. Machines. 2021. https:\/\/doi.org\/10.3390\/machines9120328.","journal-title":"Machines"},{"key":"249_CR2","doi-asserted-by":"publisher","DOI":"10.3390\/en14041081","author":"S Theocharides","year":"2021","unstructured":"Theocharides S, Theristis M, Makrides G, Kynigos M, Spanias C, Georghiou GE. Comparative analysis of machine learning models for day-ahead photovoltaic power production forecasting. Energies. 2021. https:\/\/doi.org\/10.3390\/en14041081.","journal-title":"Energies"},{"key":"249_CR3","unstructured":"Current status. Ministry of new and renewable energy, government of India, Current Status|Ministry of New and Renewable EnBEHERA2018428ergy, Government of India. https:\/\/mnre.gov.in\/solar\/current-status\/"},{"key":"249_CR4","unstructured":"Insights FB. Solar photovoltaic market size [2021\u20132028] in India, GlobeNewswire News Room. Fortune Business Insights; 2022. https:\/\/www.globenewswire.com\/news-release\/2022\/04\/25\/2428123\/0\/en\/Solar-Photovoltaic-Market-Size-2021-2028-Worth-USD-1-000-92-Billion-Exhibit-a-CAGR-25-9.html"},{"key":"249_CR5","doi-asserted-by":"publisher","unstructured":"Nziyumva E. A novel two layer stacking ensemble for improving solar irradiance forecasting. Int J Eng Res Technol. 2021. https:\/\/doi.org\/10.17577\/IJERTV10IS100138.","DOI":"10.17577\/IJERTV10IS100138"},{"key":"249_CR6","doi-asserted-by":"publisher","unstructured":"Zazoum B. Solar photovoltaic power prediction using different machine learning methods. 8th Int Conf Power Energy Syst Eng. 2021. https:\/\/doi.org\/10.1016\/j.egyr.2021.11.183.","DOI":"10.1016\/j.egyr.2021.11.183"},{"key":"249_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ref.2023.01.006","author":"D Chakraborty","year":"2023","unstructured":"Chakraborty D, et al. 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