Authors : B. Sharvani; M. M. Harshitha; Dr. Girish Kumar D.

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/muh2m3d7

Scribd : https://tinyurl.com/bd2r9dj7

DOI : https://doi.org/10.38124/ijisrt/26apr708

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The progress of digital systems and smart automation has revolutionized many industrial sectors; however, some challenges such as reliability, accuracy and real-time action are still a concern in 2019 for most types of application. Despite notable advancements advances, current methods generally suffer from problems such as scalability, adaptive ability, and robustness in practical scenarios between different framesets that restrict their real applications. To fill these holes, we propose a new improved framework to combine computational methods with learning models designed for higher efficiency and reliability power in the detector processing velocity. The suggested method delivers improved results compared to other existing approaches, meanwhile providing more consistent and reliable results during evaluation, which suggests its promising applications in practical real-world scenarios.

Keywords : Intelligent Systems, Automation, Computational Models, Performance Optimization, Real-Time Processing.

References :

1. Sharma and V. Kumar, “Machine learning-driven solar irradiance prediction for renewable energy optimization,” IEEE Access, vol. 13, pp. 11234–11245, 2025.
2. R. Singh, P. Raj and N. Verma, “Performance enhancement of solar energy forecasting using Random Forest and Gradient Boosting,” IEEE Trans. Sustain. Energy, vol. 16, no. 2, pp. 989–999, 2025.
3. L. Hassan et al., “Short-term and long-term solar irradiance forecasting using hybrid deep learning models,” IEEE Trans. Ind. Informat., vol. 21, no. 1, pp. 654–663, 2025.
4. S. Patel and R. Yadav, “Solar radiation prediction for PV output using ML algorithms,” in Proc. IEEE ICMLA, Dubai, UAE, 2025, pp. 233–239.
5. K. Turgut and M. Aslan, “Evaluation of AI-based solar radiation models for arid regions,” Renew. Energy Focus, vol. 48, pp. 21–34, 2025.
6. P. Das, “Electrical load and solar power forecasting using hybrid ANN–PSO models,” IEEE J. Emerg. Sel. Topics Power Electron., vol. 13, no. 3, pp. 1781–1790, 202s5.
7. M. Chen and Y. Wu, “Geospatial mapping of solar resources using Mapbox and satellite imagery,” IEEE Geosci. Remote Sens. Lett., vol. 22, pp. 1–5, 2025.
8. S. Prakash and D. Jain, “AI-based solar suitability assessment using environmental parameters,” IEEE Access, vol. 14, pp. 14788–14799, 2025.
9. N. Gupta, “Impact related to temperature and humidity on PV generation: A machine learning–based approach,” IEEE Trans. Energy Convers., vol. 40, no. 1, pp. 122–132, 2025.
10. A. Banerjee et al., “Cloud index-based solar output prediction using Random Forest,” IEEE Trans. Sustain. Comput., vol. 10, no. 2, pp. 311–320, 2025.