Solar Panel Parameter Monitoring System

Abstract

Solar energy has emerged as one of the most viable and sustainable alternatives to conventional fossil-fuel-based power generation. However, efficient utilization of solar panels requires continuous monitoring of their operational parameters to ensure optimal performance. This paper presents the design and implementation of an IoT-based Solar Panel Parameter Monitoring System that enables real-time acquisition and remote visualization of critical electrical and environmental parameters. The proposed system employs a NodeMCU (ESP8266) microcontroller interfaced with voltage, current, temperature (DHT11), and light intensity (LDR) sensors to measure panel output. Power is computed using the fundamental relation P = V × I. Acquired data is simultaneously displayed on a 16×2 LCD and transmitted wirelessly to the Blynk cloud platform via Wi-Fi, enabling remote monitoring through a web dashboard. The system is self-powered using a lithium-ion battery charged through a TP4056 module and regulated by an LM2596 buck converter. Experimental results demonstrate accurate parameter acquisition with latency below 2 seconds, rendering the system suitable for small-scale solar installations, research applications, and educational demonstrations.

Introduction

This study presents the design and implementation of a low-cost IoT-based solar panel monitoring system aimed at improving the efficiency and reliability of solar photovoltaic (PV) installations.

The research is motivated by the rapid global shift toward renewable energy, particularly solar power, which is widely adopted due to its clean and scalable nature. However, solar panels often suffer from performance issues such as dust accumulation, overheating, shading, and ageing, which reduce energy output. Since traditional manual inspection methods are inefficient and not scalable, the study proposes an IoT-enabled solution for real-time monitoring.

The developed system continuously measures key parameters such as voltage, current, power, temperature, and light intensity using sensors connected to a NodeMCU (ESP8266) microcontroller. The system calculates power (P = V × I) and transmits data via Wi-Fi to a cloud platform using the Blynk IoT system, where users can view real-time dashboards, graphs, and alerts through web or mobile applications. An LCD display is also included for local monitoring when internet access is unavailable.

The system architecture is divided into four layers: sensing, processing, communication, and application. It is designed to be low-cost (under ?2,000), portable, and suitable for rural or off-grid use, with fast data refresh rates and reliable sensor accuracy.

The methodology includes hardware design (solar charging, voltage regulation, and sensor integration), software development using Arduino IDE, IoT cloud integration via Blynk, and systematic module-wise testing before final deployment.

Conclusion

This paper has presented a fully functional, low-cost IoT-based Solar Panel Parameter Monitoring System built around the NodeMCU ESP8266 platform. The system successfully measures voltage, current, power (P = V × I), temperature, and light intensity in real time, presenting data locally on an LCD and remotely through the Blynk cloud dashboard. Experimental validation demonstrated parameter accuracy within ±2% and cloud transmission latency below 2 seconds, confirming the system\'s suitability for practical monitoring applications. The total component cost of approximately ?1,520 underscores its viability for small-scale and educational deployments. The architecture\'s modularity and the ESP8266\'s support for OTA firmware updates provide a robust foundation for future enhancements including AI-driven analytics and smart grid integration.

References

[1] S. Motahhir, A. El Hammoumi, and A. El Ghzizal, \"The most used MPPT algorithms: Review and the suitable low-cost embedded board for each algorithm,\" Journal of Cleaner Production, vol. 246, p. 118983, 2020. [2] M. A. Green et al., \"Solar cell efficiency tables (version 57),\" Progress in Photovoltaics: Research and Applications, vol. 29, no. 1, pp. 3–15, 2021. [3] T. Dragi?evi?, X. Lu, J. C. Vasquez, and J. M. Guerrero, \"DC microgrids — Part II: A review of power architectures, applications, and standardization issues,\" IEEE Transactions on Power Electronics, vol. 31, no. 5, pp. 3528–3549, May 2016. [4] A. Ahsan, M. T. Khan, and A. Iqbal, \"IoT-based real-time solar energy monitoring system,\" in Proc. IEEE Int. Conf. Emerging Technologies (ICET), Islamabad, Pakistan, 2019, pp. 1–6. [5] Espressif Systems, \"ESP8266EX Datasheet v6.6,\" Espressif Systems, Shanghai, China, 2020. [Online]. Available: https://www.espressif.com [6] D-Robotics UK, \"DHT11 Humidity and Temperature Sensor Datasheet,\" 2010. [Online]. Available: https://www.micropik.com/PDF/dht11.pdf [7] Blynk Inc., \"Blynk IoT Platform Documentation,\" 2023. [Online]. Available: https://docs.blynk.io [8] R. Ramabadran and B. Mathur, \"Effect of shading on series and parallel connected solar PV modules,\" Modern Applied Science, vol. 3, no. 10, pp. 32–41, 2009.

Copyright

Copyright © 2026 Rahul B. Sharlawar, Om C. Sirsath, Ankush R. Sarode, Parshuram S. Phuse, Prof. R. M. Patil. This 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.