DESIGNING A STATE-OF-THE-ART AI-DRIVEN MODEL TO ENHANCE SOLAR ENERGY PREDICTION
Tushar Gupta1*, Gagan Tiwari2, Kapil Joshi3
1Department of Computer Science, Noida International University, G.B.Nagar, Uttar Pradesh, India
2Department of Computer Science, Noida International University, G.B.Nagar, Uttar Pradesh, India
3Department of Computer Science Engineering, Uttaranchal Institute of Technology, Uttaranchal University Dehradun, India
Abstract. The increasing importance of solar energy has led to the formation of solar forecasting predictive models. Traditional methodologies find it difficult to produce accurate results due to the difficulty of the environment. New innovative AI architectures are required to overcome this challenge of high variance in prediction. This paper works on the growth of present AI architectures for solar prediction. A framework for developing an AI Architecture that is more accurate and precise based certainly on computation strategies and a mixture of disciplines: and adapts is to be developed. The investigation finds flaws, strengths, and strong supports in the current methodologies using numerous hypothetical papers. The full framework is suggested to test accuracy and adaptability and reduce the sensitivity of the proposed method using artificial neural network novel works and improved versions to make it more effortful. Furthermore, synthetic data points. is presented, and optimal algorithmic parameters are settled to the exhaustive review. It removes the traditional model’s weaknesses and increases its stability against information flaws. It shows significant results compared to methodologies, tackling data quality and model stability difficulties. Compared to methods, it reveals remarkable results, addressing hurdles such as data quality and model stability. Consequently, the proposed AI Architecture faces computational difficulties due to their very massive sizes. Accordingly, in our future research, we plan to reform this Model as guided and deploy new IoT technologies to secure socket and automatic analysis. Moreover, this research supports that the efficiency of solar energy use indirectly aligns with the sustainable development goal of Goal 9 i.e. Industry, Innovation, and Infrastructure and Goal 13 i.e. Climate Action.
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Source: International Journal of
Applied Mathematics
ISSN printed version: 1311-1728
ISSN on-line version: 1314-8060
Year: 2025
Volume: 38
Issue: 1
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