Singh, JagendraJagendraSinghNeerajUpreti, KamalKamalUpretiGaur, SonaliSonaliGaurShrivastav, A KA KShrivastavTiwari, MohitMohitTiwari2026-02-112026-02-1120249798350343878http://dx.doi.org/10.1109/ISML60050.2024.11007420https://www.scopus.com/pages/publications/105010213937https://gnanaganga.alliance.edu.in/handle/123456789/9596This research endeavors to revolutionize the precision of photovoltaic (PV) power generation predictions through the introduction of a novel Hybrid Ensembled Model. Motivated by the imperative to enhance the reliability of renewable energy technologies, the study amalgamates the strengths of fuzzy logic, support vector regression, ensemble learning, and evolutionary computation. The model's design addresses the inherent challenges of PV power generation prediction, notably uncertainties and non-linear dynamics. The investigation unfolds in distinct phases, beginning with a meticulous exploration of the significance of precise PV power generation prediction. Acknowledging the limitations of existing methodologies, the research establishes a robust foundation for the development of the Hybrid Ensembled Model. Methodologically, the incorporation of Double-Input-Fuzzy-Modules (DIFM), Extreme Learning Machine (ELM), and evolutionary computation ensures a comprehensive and adaptive framework. This research propels the discourse on renewable energy prediction models, presenting a Hybrid Ensembled Model as a pioneering solution for precise and reliable PV power generation forecasts. The implications extend beyond academia, envisioning a future where sustainable energy systems are underpinned by accurate predictions, fostering optimal resource utilization and contributing to a resilient and sustainable energy landscape. © 2024 IEEE.enEnsemble LearningFuzzy LogicHybrid Ensembled ModelPv Power GenerationRenewable Energy PredictionConference paper