Artificial Intelligence Applications in Dc Solar Air Coolers: Enhancing Performance, Efficiency, and User Experience
Authors
Rajasthan Technical University, Kota-324010, Rajasthan, India (India)
Article Information
DOI: 10.51244/IJRSI.2026.1303000039
Subject Category: Engineering
Volume/Issue: 13/3 | Page No: 446-458
Publication Timeline
Submitted: 2026-03-06
Accepted: 2026-03-12
Published: 2026-03-26
Abstract
The merger of Artificial Intelligence (AI) and renewable energy technologies is changing the face of the sustainable cooling technology space. Among the technologies being developed in this space include DC Solar Air Coolers that are air cooling technologies that utilize Direct Current (DC) electricity that can be produced using Solar PV Panels. Solar Air Coolers are experiencing setbacks in terms of energy efficiency techniques and adaptability to environmental variability. There are also setbacks concerning maintenance and operation. The adoption of AI technologies has presented an opportunity for the development of DC Solar Air Coolers that are adaptable and fit for sustainable and efficient cooling.
Keywords
The integration of Artificial Intelligence (AI) in renewable sources of energy is revolutional in the way
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References
1. Kaiprath, J., V. V., K. A review on solar photovoltaic-powered thermoelectric coolers, performance enhancements, and recent advances. Int. J. Air-Cond. Ref. 31, 6 (2023). https://doi.org/10.1007/s44189-023-00022-y [Google Scholar] [Crossref]
2. V. Palomba, U. Wittstadt, A. Bonanno, M. Tanne, N. Harborth, and S. Vasta , "Components and design guidelines for solar cooling systems: The experience of ZEOSOL," Renewable Energy, vol. 141, pp. 678-692, 2019 . doi: 10.1016/j.renene.2019.04.018. [Google Scholar] [Crossref]
3. B.-J. Huang, T.-F. Hou, P.-C. Hsu, T.-H. Lin, Y.-T. Chen, C.-W. Chen, K. Li, and K. Y. Lee, "Design of direct solar PV driven air conditioner," Renewable Energy, vol. 88, pp. 95-101, Apr. 2016, doi: 10.1016/j.renene.2015.11.026. [Google Scholar] [Crossref]
4. T. Narayane B, A. Lavanya, P. Thevamudhan, M. A. Javad, and M. Vijayalaxmi, "BLDC Motor Driven Solar Powered Air-Cooling System," 2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP), 2022, doi: 10.1109/ICICCSP53532.2022.9862490. [Google Scholar] [Crossref]
5. G Pavithra, Prajusha P J, Raghul S, J Kandasamy, "Artificial Intelligence Based Maximum Power Point Tracking Algorithm for Solar Photovoltaic System," in Proc. 2025 Int. Conf. on Electronics, Computing, Communication and Control Technology (ICECCC), May 2025. doi: 10.1109/ICECCC65144.2025.11064258. [Google Scholar] [Crossref]
6. Vineeth Kumar P.K., Jijesh J.J. Comparative Analysis of Conventional and Artificial Intelligence-based Maximum Power Point Tracking Algorithms for Solar Photovoltaic Applications, International Journal of Computer Applications (0975 – 8887) Volume 184 – No. 42, January 2023 [Google Scholar] [Crossref]
7. K. Al Sayed, A. Boodi, R. S. Broujeny, and K. Beddiar, "Reinforcement learning for HVAC control in intelligent buildings: A technical and conceptual review," J. Build. Eng., vol. 95, Art. no. 110085, 2024, doi: 10.1016/j.jobe.2024.110085. [Google Scholar] [Crossref]
8. G. U. Bekal, A. Ghareeb, and A. Pujari, "Continual Reinforcement Learning for HVAC Systems Control: Integrating Hypernetworks and Transfer Learning," March 2025. doi: 10.48550/arXiv.2503.19212. [Google Scholar] [Crossref]
9. Linge A., S. Timande, A. Pande, A. Motherao, S. Dhurve, P. Bhoyar, and A. Dongare, "Innovative Smart Cooler: A Step Towards a Sustainable Future," International Journal of Advanced Innovative Technology in Engineering, vol. 10, no. 2, pp. 15-19, Mar. 2025. [Google Scholar] [Crossref]
10. Gassar A. A. A. and R. Jafar, "Artificial Intelligence-Enabled Heating, Ventilation, and Air Conditioning Systems Toward Zero-Emission Buildings: A Systematic Review of Applications, Challenges, and Future Directions," Appl. Sci., vol. 15, no. 19, p. 10497, Sep. 2025, doi: 10.3390/app151910497. [Google Scholar] [Crossref]
11. M. M. S. Mukundaswamy, C. Rajinikanth, and C. B. Shankarlingappa, "Automated Clean and Cooling System for Solar Photovoltaic Panels using IoT," 2024 4th International Conference on Data Engineering and Communication Systems (ICDECS), 2024, doi: 10.1109/ICDECS59733.2023.10502471. [Google Scholar] [Crossref]
12. R. N. Sonawane, A. S. Ghule, A. P. Bowlekar, and A. H. Zakane, "Design and Development of Temperature and Humidity Monitoring System," Agricultural Science Digest, vol. 39, no. 2, pp. 114-118, Apr.-Jun. 2019, doi: 10.18805/ag.D-4893. [Google Scholar] [Crossref]
13. D. K. Sharma, A. P. Singh, and V. Verma, "A review of solar energy: Potential, status, targets and challenges in Rajasthan," Int. J. Eng. Res. Technol., vol. 3, no. 3, pp. 668–672, Mar. 2014. [Google Scholar] [Crossref]
14. R. Gupta, A. K. Yadav, S. K. Jha, and P. K. Pathak, "Predicting global horizontal irradiance of north central region of India via machine learning regressor algorithms," Eng. Appl. Artif. Intell., vol. 133, part E, art. no. 108426, Jul. 2024, doi: 10.1016/j.engappai.2024.108426. [Google Scholar] [Crossref]
15. Y. Ledmaoui, A. E. Maghraoui, M. E. Aroussi, and R. Saadane, "Review of Recent Advances in Predictive Maintenance and Cybersecurity for Solar Plants," Sensors, vol. 25, no. 1, art. no. 206, Jan. 2025. [Google Scholar] [Crossref]
16. O. Mhatre, "Solar Module Certification in India: A Detailed Guide to BIS, IEC, ISO, and IECEE," Volt Roam, Apr. 14, 2025. [Online]. Available: https://www.omkarmhatre.in/2025/04/solar-module-certification-in-india.html. [Google Scholar] [Crossref]
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