Data-Driven Analysis of Solar Energy Harvesting Systems: Performance Assessment, Environmental Influences, and Future Perspectives
DOI:
https://doi.org/10.69980/z9b67818Keywords:
Solar Energy Harvesting, Photovoltaic Systems, Renewable EnergyAbstract
Solar photovoltaic (PV) systems have emerged as an important part of the global energy transition in the face of increasing demand for sustainable and clean energy solutions. But the environmental conditions greatly affect the performance of solar energy harvesting systems, which require detailed analysis with the support of data for better efficiency and reliability in the operation of such systems. This research introduces a data-driven evaluation of solar energy harvesting systems based on a combined solar PV generation and weather sensor information from two solar power plants. Key operational factors such as the DC power, AC power, daily yield and total yield were analysed together with the environmental factors such as solar irradiation, ambient temperature and module temperature. Performance evaluation of the system was carried out using descriptive, comparative and correlation analysis to study the impact of environmental factors on photovoltaic energy generation. The findings showed that solar irradiation is the most important factor influencing power output as it has a high positive correlation with the photovoltaic (PV) generation. Also, it was determined that the ambient and module temperature had an influence on the system efficiency – which might lead to a decrease in actual performance if the module temperature rises. It was observed that the operational characteristics of both solar plants are similar and environmental conditions play a significant role in determining the energy harvesting results. The results show the potentials of data-driven solutions in photovoltaic performance assessment and renewable energy optimization
References
1. Ahsan, F., Dana, N. H., Sarker, S. K., Li, L., Muyeen, S. M., Ali, M. F., ... & Das, P. (2023). Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review. Protection and Control of Modern Power Systems, 8(3), 1-42.
2. Al-Dahidi, S., Madhiarasan, M., Al-Ghussain, L., Abubaker, A. M., Ahmad, A. D., Alrbai, M., ... & Zio, E. (2024). Forecasting solar photovoltaic power production: A comprehensive review and innovative data-driven modeling framework. Energies, 17(16), 4145.
3. Bachmann, N., Tripathi, S., Brunner, M., & Jodlbauer, H. (2022). The contribution of data-driven technologies in achieving the sustainable development goals. Sustainability, 14(5), 2497.
4. Baset, A., & Jradi, M. (2024). Data-driven decision support for smart and efficient building energy retrofits: A review. Applied System Innovation, 8(1), 5.
5. Bertozzi, O., Chamorro, H. R., Gomez‐Diaz, E. O., Chong, M. S., & Ahmed, S. (2024). Application of data‐driven methods in power systems analysis and control. IET Energy Systems Integration, 6(3), 197-212.
6. Chen, G., Lu, S., Zhou, S., Tian, Z., Kim, M. K., Liu, J., & Liu, X. (2025). A systematic review of building energy consumption prediction: From perspectives of load classification, data-driven frameworks, and future directions. Applied Sciences, 15(6), 3086.
7. Di Stefano, A. G., Ruta, M., & Masera, G. (2023). Advanced digital tools for data-informed and performance-driven design: a review of building energy consumption forecasting models based on machine learning. Applied Sciences, 13(24), 12981.
8. Ding, Y., Cui, R., Lu, X., Xu, J., Meng, X., Wang, F., ... & Fu, Z. (2023, November). A Review of Multi-source Data-driven PV Facilities in Agricultural Regions for Low-Carbon Operation and Decision-making. In Proceedings of the 2023 3rd International Conference on Big Data, Artificial Intelligence and Risk Management (pp. 965-970).
9. Eze, V. H. U., Eze, E. C., Alaneme, G. U., BUBU, P. E., Nnadi, E. O. E., & Okon, M. B. (2025). Integrating IoT sensors and machine learning for sustainable precision agroecology: enhancing crop resilience and resource efficiency through data-driven strategies, challenges, and future prospects. Discover Agriculture, 3(1), 83.
10. Gupta, A. K., & Singh, R. K. (2025). A review of the state of the art in solar photovoltaic output power forecasting using data-driven models. Electrical Engineering, 107(4), 4727-4770.
11. Hassane, A. I., Herve, S. A., Alphonse, H., Todou, D. E., Aurelien, Y., Fidel, M. V., ... & Luc, N. J. (2024). Data Driven Analysis of the Impact of Weather Parameters on Solar Photovoltaic Panels Efficiency in a Sahel Region: Future Prospects. Applied Solar Energy, 60(2), 313-327.
12. Howlader, A. S. (2025). Machine learning applications in renewable energy: Predictive analytics for solar cell performance optimization and energy yield forecasting. American Journal of Scholarly Research and Innovation, 4(01), 392-427.
13. Kannal, A. (2020). Solar Power Generation Data [Data set]. Kaggle. https://www.kaggle.com/datasets/anikannal/solar-power-generation-data
14. Kumari, R., Kumhar, R., & Deo, R. N. (2024, August). Data Driven Approaches to Improve Solar Energy Forecasting Using Machine Learning Algorithm. In 2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON) (pp. 1-7). IEEE.
15. Lin, Y., Tang, J., Guo, J., Wu, S., & Li, Z. (2025). Advancing AI-enabled techniques in energy system modeling: a review of data-driven, mechanism-driven, and hybrid modeling approaches. Energies, 18(4), 845.
16. Luo, S. L., Shi, X., & Yang, F. (2024). A review of data-driven methods in building retrofit and performance optimization: From the perspective of carbon emission reductions. Energies, 17(18), 4641.
17. Mahmood, A., & Wang, J. L. (2021). Machine learning for high performance organic solar cells: current scenario and future prospects. Energy & environmental science, 14(1), 90-105.
18. Mohammad, A., & Mahjabeen, F. (2023). Revolutionizing solar energy with ai-driven enhancements in photovoltaic technology. BULLET: Jurnal Multidisiplin Ilmu, 2(4), 1174-1187.
19. Moradi, J., Shahinzadeh, H., Nafisi, H., Marzband, M., & Gharehpetian, G. B. (2019, December). Attributes of big data analytics for data-driven decision making in cyber-physical power systems. In 2020 14th international conference on protection and automation of power systems (IPAPS) (pp. 83-92). IEEE.
20. Ning, K. (2021). Data driven artificial intelligence techniques in renewable energy system (Doctoral dissertation, Massachusetts Institute of Technology).
21. Ohalete, N. C., Aderibigbe, A. O., Ani, E. C., Ohenhen, P. E., Daraojimba, D. O., & Odulaja, B. A. (2023). AI-driven solutions in renewable energy: A review of data science applications in solar and wind energy optimization. World Journal of Advanced Research and Reviews, 20(3), 401-417.
22. Perera, S. M., Putrus, G., Conlon, M., Narayana, M., & Sunderland, K. (2022). Wind energy harvesting and conversion systems: A technical review. Energies, 15(24), 9299.
23. Saadane, R., Chehri, A., & Jeon, S. (2022). AI-based modeling and data-driven evaluation for smart farming-oriented big data architecture using IoT with energy harvesting capabilities. Sustainable Energy Technologies and Assessments, 52, 102093.
24. Strielkowski, W., Vlasov, A., Selivanov, K., Muraviev, K., & Shakhnov, V. (2023). Prospects and challenges of the machine learning and data-driven methods for the predictive analysis of power systems: A review. Energies, 16(10), 4025.
25. Stüber, M., Scherhag, F., Deru, M., Ndiaye, A., Sakha, M. M., Brandherm, B., ... & Frey, G. (2021). Forecast quality of physics-based and data-driven pv performance models for a small-scale PV system. Frontiers in Energy Research, 9, 639346.
26. Sun, L., & You, F. (2021). Machine learning and data-driven techniques for the control of smart power generation systems: An uncertainty handling perspective. Engineering, 7(9), 1239-1247.
27. Tsolakis, A. C., Kalamaras, I., Vafeiadis, T., Zyglakis, L., Bintoudi, A. D., Chouliara, A., ... & Tzovaras, D. (2020). Towards a Holistic Microgrid Performance Framework and a Data-Driven Assessment Analysis. Energies, 13(21), 5780.
28. Unni, A., & Channi, H. K. (2026). Data Analytics for Performance Optimization in Renewable Energy. Optimization in Sustainable Energy: Methods and Applications, 291-327.
29. Verma, S., Kameswari, Y. L., & Kumar, S. (2024). A review on environmental parameters monitoring systems for power generation estimation from renewable energy systems. BioNanoScience, 14(4), 3864-3888.
30. Yadav, S., Garg, D., & Luthra, S. (2021). Development of IoT based data-driven agriculture supply chain performance measurement framework. Journal of Enterprise Information Management, 34(1), 292-327
