Artificial Intelligence Driven Solar Tracking System for Enhanced Energy Efficiency and Environmental Sustainability - Springer Nature Link

Abstract

This work suggests an intelligent PV solar tracking system based on a single-axis, which is a combination of Artificial Neural Network (ANN) with Fuzzy Logic Controller (FLC). The ANN determines the tracking angle based on the time and geographical inputs, whereas the FLC adjusts the control actions of the DC motor so that it gives the accurate orientation of the panels. A FLC is incorporated to enhance actuator precision and stability. The proposed system was able to achieve a prediction accuracy of 98%, with a MSE value of 0.0012, demonstrating that the prediction of the best tracking angle can be reliably predicted in various environments. Compared to traditional fixed and sensor-based tracking systems, the intelligent tracking system with a single axis provides increased efficiency of solar energy harvesting, increased system stability, and reduced actuator movement.

REFERENCES

- Aboelmagd, M.R., Selim, A., and Abdel-Akher, M., Forecasting of power generation in a single-axis solar tracking PV system using an enhanced artificial neural network-based method, IET Renew. Power Gener., 2025, vol. 19, no. 1, e70100. https://doi.org/10.1049/rpg2.70100

- Agrawal, M., Peterson, J.C., and Griffiths, T.L., Using machine learning to guide cognitive modeling: A case study in moral reasoning, arXiv:1902.06744, 2019.

- Bhatia, Y., Rai, R., Gupta, V., Aggarwal, N., and Akula, A., Convolutional neural networks based potholes detection using thermal imaging, J. King Saud Univ.—Comput. Inf. Sci., 2019, vol. 34, no. 3, pp. 578–588. https://doi.org/10.1016/j.jksuci.2019.12.003

- Essa, M.E.S.M., Hassan, A.A., El-Kholy, E.E., and Ahmed, M.M.R., Innovations and advancements in solar tracker systems: A comprehensive review, J. Mechatron. Electr. Power Veh. Technol., 2025, vol. 16, no. 1. https://doi.org/10.14203/j.mev.2025.v16.1-10

- Ikhwan, M., Mardlijah, M., and Imron, C., Model predictive control on dual axis solar tracker using Matlab/Simulink simulation, in Proc. Int. Conf. Inf. Commun. Technol. (ICOIACT), Yogyakarta, Indonesia, 2018, pp. 784–788. https://doi.org/10.1109/ICOIACT.2018.8350766

- Katrandzhiev, N.T. and Karnobatev, N.N., Algorithm for single axis solar tracker, in Proc. IEEE XXVII Int. Sci. Conf. Electron.—ET, Sozopol, Bulgaria, 2018, pp. 1–4. https://doi.org/10.1109/ET.2018.8549572

- Kolamroudi, M.K., Jaiyeoba, O.H., Ilkan, M., and Safaei, B., A comprehensive review on the artificial intelligence for the development of thermal concentrating photovoltaic systems, Sol. Energy, 2025, vol. 301, 113937. https://doi.org/10.1016/j.solener.2025.113937

- Makhloufi, M.T., Khireddine, M.S., Abdessemed, Y., and Boutarfa, A., Tracking power photovoltaic system using artificial neural network control strategy, Int. J. Intell. Syst. Appl., 2014, vol. 6, no. 12, pp. 17–26. https://doi.org/10.5815/ijisa.2014.12.03

- Makwanna, A. and Lad, D., Dual axis solar tracking using 5-LDR sensor, Int. J. Curr. Eng. Technol., 2017, vol. 7, pp. 1750–1753.

- Mamodiya, U., Kishor, I., Ganguly, P., Mukherjee, I., and Naik, N., A machine learning approach to assess the climate change impacts on single and dual-axis tracking photovoltaic systems, Sci. Rep., 2025, vol. 15, no. 1, 24910. https://doi.org/10.1038/s41598-025-70892-0

- Meenal, R. and Immanuel Selvakumar, A., Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters, Renew. Energy, 2018, vol. 121, pp. 324–343. https://doi.org/10.1016/j.renene.2018.01.057

- Mehdi, G., Ali, N., Hussian, S., Zaidi, A.A., Shah, A.H., and Azeem, M.M., Design and fabrication of automatic single axis solar tracker for solar panel, in Proc. 2nd Int. Conf. Comput. Math. Eng. Technol. (iCoMET), Sukkur, Pakistan, 2019, pp. 1–4. https://doi.org/10.1109/ICOMET.2019.8673438

- Nsengiyumva, W., Chen, S.G., Hu, L., and Chen, X., Recent advancements and challenges in solar tracking systems (STS): A review, Renew. Sustain. Energy Rev., 2018, vol. 81, pp. 250–279. https://doi.org/10.1016/j.rser.2017.07.058

- Paliyal, P.S., Mondal, S., Layek, S., Kuchhal, P., and Pandey, J.K., Automatic solar tracking system: A review pertaining to advancements and challenges in the current scenario, Clean Energy, 2024, vol. 8, no. 6, pp. 237–262. https://doi.org/10.1093/ce/zkad092

- Santos de Araújo, J.V., de Lucena, M.P., da Silva Netto, A.V., Gomes, F.D.S.V., Oliveira, K.C.D., de Souza Neto, J.M.R., Cavalcante, S.L., Morales, L.R.V., Villanueva, J.M.M., and Macedo, E.C.T.D., Solar tracking control algorithm based on artificial intelligence applied to large-scale bifacial photovoltaic power plants, Sensors, 2024, vol. 24, no. 12, 3890. https://doi.org/10.3390/s24123890

- Sawant, A., Bondre, D., Joshi, A., Tambavekar, P., and Deshmukh, A., Design and analysis of automated dual axis solar tracker based on light sensors, in Proc. 2nd Int. Conf. I-SMAC (IoT Soc. Mobile Anal. Cloud), Palladam, India, 2018, pp. 454–459. https://doi.org/10.1109/I-SMAC.2018.8653668

- Sidek, M.H.M., Azis, N., Hasan, W.Z.W., AbKadir, M.Z.A., Shafie, S., and Radzi, M.A.M., Automated positioning dual-axis solar tracking system with precision elevation and azimuth angle control, J. Renew. Sustain. Energy, 2017, vol. 124, pp. 160–170.

- Tharamuttam, J.K. and Ng, A.K., Design and development of an automatic solar tracker, Energy Procedia, 2017, vol. 143, pp. 629–634. https://doi.org/10.1016/j.egypro.2017.12.759

- Yang, X., Zou, T., Xu, Y., Chen, F., Luo, H., and Li, R., Research dynamics and applications of tracking technology and devices in solar energy utilization system, Sustain. Energy Technol. Assess., 2025, vol. 75, 104256. https://doi.org/10.1016/j.seta.2025.104256

Funding

This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

Author information

Authors and Affiliations

Corresponding author

Ethics declarations

The authors of this work declare that they have no conflicts of interest.

Additional information

Publisher’s Note.

Pleiades Publishing remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

AI tools may have been used in the translation or editing of this article.

About this article

Cite this article

Varadharajan, D.P., Kumarasamy, S. Artificial Intelligence Driven Solar Tracking System for Enhanced Energy Efficiency and Environmental Sustainability. Autom Remote Control 87, 249–260 (2026). https://doi.org/10.1134/S000511792560106X

- Received:

- Revised:

- Accepted:

- Published:

- Version of record:

- Issue date:

- DOI: https://doi.org/10.1134/S000511792560106X