A Review Paper on Intelligent IoT Platform for Performance Analysis of Solar-Wind Hybrid Systems

  • Unique Paper ID: 201981
  • Volume: 12
  • Issue: 12
  • PageNo: 7175-7178
  • Abstract:
  • The Intelligent IoT Platform for Performance Analysis of Solar–Wind Hybrid Systems integrates smart sensors, cloud connectivity, and real-time data analytics to evaluate and optimize the combined performance of solar and wind energy sources. By enabling continuous monitoring, fault detection, and efficiency assessment, the platform enhances system reliability, energy output, and overall operational sustainability. The rapid growth of renewable energy has increased the adoption of hybrid solar–wind systems to ensure reliable and sustainable power generation. However, managing and optimizing the performance of these systems remains challenging due to the intermittent nature of solar and wind resources. This study presents an Intelligent IoT Platform for Performance Analysis of Solar–Wind Hybrid Systems, which integrates real-time monitoring, data analytics, and predictive control. The platform collects operational data including voltage, current, power output, irradiance, wind speed, and battery state of charge through IoT-enabled sensors and transmits it to a cloud-based analytics framework. Using advanced algorithms, the system performs performance evaluation, fault detection, and energy optimization, enabling proactive maintenance and improved reliability.

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{201981,
        author = {SAUD SIKANDAR TAMBOLI and PRAJYOT SHARAD PATIL and Prof.Suraj S.Shinde},
        title = {A Review Paper on Intelligent IoT Platform for Performance Analysis of Solar-Wind Hybrid Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {7175-7178},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201981},
        abstract = {The Intelligent IoT Platform for Performance Analysis of Solar–Wind Hybrid Systems integrates smart sensors, cloud connectivity, and real-time data analytics to evaluate and optimize the combined performance of solar and wind energy sources. By enabling continuous monitoring, fault detection, and efficiency assessment, the platform enhances system reliability, energy output, and overall operational sustainability. The rapid growth of renewable energy has increased the adoption of hybrid solar–wind systems to ensure reliable and sustainable power generation. However, managing and optimizing the performance of these systems remains challenging due to the intermittent nature of solar and wind resources. This study presents an Intelligent IoT Platform for Performance Analysis of Solar–Wind Hybrid Systems, which integrates real-time monitoring, data analytics, and predictive control. The platform collects operational data including voltage, current, power output, irradiance, wind speed, and battery state of charge through IoT-enabled sensors and transmits it to a cloud-based analytics framework. Using advanced algorithms, the system performs performance evaluation, fault detection, and energy optimization, enabling proactive maintenance and improved reliability.},
        keywords = {Cloud Monitoring, Data Analytics, ESP32, Hybrid Renewable Energy System, Internet of Things (IoT), Performance Analysis, Remote Monitoring, Solar-Wind Hybrid System, Smart Energy Management, Wireless Sensor Network.},
        month = {May},
        }

Cite This Article

TAMBOLI, S. S., & PATIL, P. S., & S.Shinde, P. (2026). A Review Paper on Intelligent IoT Platform for Performance Analysis of Solar-Wind Hybrid Systems. International Journal of Innovative Research in Technology (IJIRT), 12(12), 7175–7178.

Related Articles