Performance Monitoring and Evaluation of Solar Photovoltaic System under Adverse Environmental Condition

Oluwadare Raphael Olakunle, Emmanuel Ajadi, Olusola Oloruntoba

Abstract


Environmental condition affects the output performance of solar photovoltaic (PV) system. In this study, IoT optimization methods were used to improve and monitor the performance of a small-scale solar PV system. The potential of solar tracking, condition monitoring and output prediction modelling were combined and harnessed to determine the output performance of the solar PV system under adverse environmental condition. An Arduino Uno microcontroller system using Light Dependent Resistor (LDR) and DHT11 sensors was developed for solar tracking and condition monitoring of a 20 W solar PV panel with rated open-circuit voltage of 21.6 V. ThingSpeak IoT platform was used for the real-time condition monitoring of the solar PV system in which output voltage, temperature and humidity were recorded. A Multiple linear regression (MLR) model equation was developed using the collected data from the IoT. The result obtained showed that solar PV panel exposed to an ambient temperature of 26 to 35 0C is sufficient to yield a stable maximum output voltage of 20 V bounded within the margin of ± 0.5 at humidity level below 50%. The predicted output voltage based on the MLR model equation showed high accuracy when compared with the measured output voltage data with percentage reduction of 0.79 to 9.42 % from the maximum measured output voltage recorded at any time period. However, when compared with the open-circuit output voltage of the solar panel, it ranges from 7.91 to 20.18 %. This will help in proper load management during cloudy weather condition.


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Akhtar, I., Asim, M., Yadav, R. K., Agarwal, P., and Kirmani, S. (2020). Design of effective grid-connected solar system. Springer Nature, 98, 606-614. https://doi.org/10.1007/978-3-030-33846-6_64

Rajendran, G., Raute, R., and Caruana, C. (2025). A comprehensive review of solar PV integration with smart-grids: challenges, standards, and grid codes. Energies, 18, 1-80. https://doi.org/10.3390/en18092221J.

Gopal, M., Chandra, P. T., Venkata, R. N., and Yadav, B. P. (2020). IoT based solar power monitoring system. IOP Conf. Series: Materials Science and Engineering, 981, 1-7. https://doi.org/10.1088/1757-899X/981/3/032037

Li, L.-L., Wen, S.-Y., Tseng, M.-L., and Wang, C.-S. (2019). Renewable energy prediction: A novel short-term prediction model of photovoltaic output power. Journal of Cleaner Production, 228, 359-375. https://doi.org/10.1016/j.jclepro.2019.04.331

Alombah, N. H., Mungwe, J. N., Harrison, A., Mbasso, W. F., and Fotsin, H. B. (2025). Advanced IoT-based monitoring system for real-time photovoltaic performance evaluation: conception, development and experimental validation. Scientific African, 28, 1-20. https://doi.org/10.1016/j.sciaf.2025.e02763

Zidane, T. E., Aziz, A. S., Zahraoui, Y., Kotb, H., Aboras, K. M., Kitmo, and Jember, Y. B. (2023). Grid-connected solar PV power plants optimization: A Review. IEEE Access, 11, 79588-79608. https://doi.org/10.1109/ACCESS.2023.3299815

Reza, M. N., Mondol, N., Hossain, M. S., and Kabir, M. A. (2021). Design and implementation of an automatic single axis solar tracking system to enhance the performance of a solar photovoltaic panel. 2021 International Conference on Science & Contemporary Technologies (ICSCT) (pp. 1-6). Bangladesh:IEEE. https://doi.org/10.1109/ACMI53878.2021.9528189

Mani, P., Sunagar, P., A., M. N., D., R., and Kathir, I. (2022). IoT-based solar panel tracking system to enhance the output power. Proceedings of the Third International Conference on Smart Electronics and Communication (ICOSEC 2022) (pp. 488-493). India: IEEE. https://doi.org/10.1109/ICOSEC54921.2022.9952012

Hammoumi, A. E., Motahhir, S., Ghzizal, A. E., and Derouich, A. (2021). Internet of Things-based solar tracker system. Green Energy and Technology, 75-95. https://doi.org/10.1007/978-3-030-64565-6_4

Boucif, O. H., Lahouaou, A. M., Boubiche, D. E., and Toral-Cruz, H. (2025). Artificial Intelligence of Things for solar energy monitoring and control. Applied Sciences, 15, 1-57. https://doi.org/10.3390/app15116019

Rao, C. K., Sahoo, S. K., and Yanine, F. F. (2024). An IoT-based intelligent smart energy monitoring system for solar PV power generation. Energy Harvesting and Systems, 11(1), 1-13. https://doi.org/10.1515/ehs-2023-0015

Demir, B. E. (2023). A new low-cost Internet of Things-based monitoring system design for stand-alone solar photovoltaic plant and power estimation. Applied Sciences, 13, 1-12. https://doi.org/10.3390/app132413072

Sajjad, S. H., Sajib, M. E., Hasan, M. N., and Razzak, M. A. (2023). Design and implementation of an IoT based solar power monitoring system. Proceedings of the 2023 IEEE AI and IOT Congress (pp. 768-773). Bangladesh: IEEE. https://doi.org/10.1109/AIIoT58121.2023.10174311

Sarkar, S. A., Nimbalkar, A. D., and More, S. K. (2024). Design of solar panel monitoring system using ESP32 & IOT. 2024 IEEE International Conference on Smart Power Control and Renewable Energy (ICSPCRE) (pp. 1-6). India: IEEE. https://doi.org/DOI: 10.1109/ICSPCRE62303.2024.10675117

Inayah, I., Hayati, N., Nurcholis, A., Dimyati, A., and Prasetia, M. G. (2022). Realtime monitoring system of solar panel performance based on Internet of Things using Blynk application. Electronics, Informatics, and Vocational Education,7(2),135-143. https://doi.org/10.21831/elinvo.v7i2.53365

Mamun, A. A., Shuvo, M. H., Islam, T., Islam, D., Islam, M. J., and Tanvir, F. A. (2024). Developed an IoT-based smart solar energy monitoring system for environmental sustainability. 2024 3rd International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE), 25-27 April, 2024, Gazipur, Bangladesh (pp. 1-6). Bangladesh: IEEE. https://doi.org/10.1109/ICAEEE62219.2024.10561689

Tripathi, A. K., Sharma, N. K., Pavan, J., and Bojjagania, S. (2022). Output Power Prediction of Solar Photovoltaic Panel Using Machine Learning Approach. International Journal of Electrical and Electronics Research (IJEER), 10(4), 779-783. https://doi.org/10.37391/IJEER.100401


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