Published on: April 2026
SMART SOLAR POWER FORECASTING AND MONITORING PLATFORM
Vasamsetti Sravanthi Tula Ramya Pilla Gayathri Akula Karuna
Dr.S.Srikanth
Article Status
Available Documents
Abstract
The Smart Solar Power Forecasting and Monitoring Platform is designed to provide real-time monitoring and predictive analysis of solar energy systems. With the increasing adoption of renewable energy, efficient monitoring and accurate forecasting have become essential to ensure optimal energy utilization and system performance.
This project integrates both monitoring and forecasting functionalities into a single platform. The monitoring system tracks key solar parameters such as voltage, current, and power in real time using a web-based dashboard. The backend is developed using Spring Boot, which generates and processes solar data through REST APIs, while the frontend visualizes the data using interactive charts.
How to Cite this Paper
Sravanthi, V., Ramya, T., Gayathri, P. & Karuna, A. (2026). Smart Solar Power Forecasting and Monitoring Platform. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.517
Sravanthi, Vasamsetti, et al.. "Smart Solar Power Forecasting and Monitoring Platform." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.517.
Sravanthi, Vasamsetti,Tula Ramya,Pilla Gayathri, and Akula Karuna. "Smart Solar Power Forecasting and Monitoring Platform." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.517.
References
- A. Duffie and W. A. Beckman, Solar Engineering of Thermal Processes, Wiley, 2013.
- Kalogirou, Solar Energy Engineering: Processes and Systems, Academic Press, 2014.
- Huld, R. Gottschalg, H. Beyer, and M. Topič, “A power-prediction model for solar energy,” Solar Energy, vol. 84, no. 2, pp. 324–333, 2010.
- Mellit and S. Kalogirou, “Artificial intelligence techniques for photovoltaic applications,” Renewable Energy, vol. 33, no. 6, pp. 125–134,2008.
- Abuella and B. Chowdhury, “Solar power forecasting using machine learning techniques,” IEEE PES General Meeting, 2015.
- Yang, P. Jirutitijaroen, and W. M. Walsh, “Hourly solar irradiance time series forecasting using cloud cover index,” Solar Energy, 2012.
- Yona, T. Senjyu, and T. Funabashi, “Application of neural network for forecasting solar radiation,” Renewable Energy, 2013.
- Pedro and R. Coimbra, “Assessment of forecasting techniques for solar power production,” Renewable Energy, 2012.
- Voyant et al., “Machine learning methods for solar radiation forecasting,” Renewable Energy, vol. 105, pp. 569–582, 2017.
- Diagne et al., “Review of solar irradiance forecasting methods,” Renewable and Sustainable Energy Reviews, 2013.
Ethical Compliance & Review Process
- •All submissions are screened under plagiarism detection.
- •Review follows editorial policy.
- •Authors retain copyright.
- •Peer Review Type: Double-Blind Peer Review
- •Published on: Apr 20 2026
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.

