International Journal of Computational and Electronic Aspects in Engineering
Volume 7 · Issue 4 · August 2026 · pp. 326-337
Article · Peer Reviewed
Received: May 16, 2026 · Accepted: July 23, 2026 · Published: August 01, 2026
DOI: 10.26706/ijceae.7.4.20260802
Open Access · CC BY 4.0

Improving Outdoor Unit Efficiency of Air-Conditioning Systems in Hot Climates Using Artificial Intelligence

Omer Muwafaq Mohammed Ali*
Refrigeration and Air Conditioning Engineering Department, Engineering Technical College, AL-Turath University, Baghdad, Iraq.

*Corresponding author: oma.aldabagh1971@gmail.com
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Abstract

Air-conditioning (AC) systems in high-temperature environments suffer serious performance loss because elevated ambient temperatures reduce the condenser's heat rejection capability, thereby increasing compressor energy consumption and mechanical stress on outdoor unit components. This problem is especially severe in countries such as Iraq, where summer temperatures frequently exceed 45 °C. This paper proposes an artificial intelligence (AI)-assisted performance enhancement system for air-conditioning outdoor units operating under high ambient temperatures. A thermodynamic and mechanical model of the vapor-compression refrigeration cycle is developed to evaluate system performance under varying environmental conditions. The proposed AI module functions as a rule-based optimization tool that dynamically adjusts condenser fan speed and compressor loading to maintain stable operation and improve heat rejection efficiency. Simulation results demonstrate that the proposed AI-based adaptive control strategy improves the coefficient of performance (COP), reduces compressor power consumption, and lowers condenser temperature compared with conventional fixed-parameter control. The findings indicate that integrating AI into existing air-conditioning systems can significantly enhance energy efficiency and operational reliability in extremely hot climates without requiring substantial hardware modifications.

Keywords

Air-conditioning system Outdoor unit Hot climate High ambient temperature Condensing unit Artificial intelligence Energy efficiency

References

  1. K. Gupta, L. K. Awasthi, and R. Kumar, “UAV-Based Surveillance Systems: A Review,” International Journal of Advanced Research in Computer Science, vol. 8, no. 5, pp. 123–129, 2017.
  2. S. Waharte and N. Trigoni, “Supporting Search and Rescue Operations with UAVs,” in Proc. IEEE Int. Conf. Emerging Security Technologies, 2010.
  3. Y. Zeng, R. Zhang, and T. J. Lim, “Wireless Communications with Unmanned Aerial Vehicles: Opportunities and Challenges,” IEEE Communications Magazine, vol. 54, no. 5, pp. 36–42, 2016.
  4. I. Bekmezci, O. K. Sahingoz, and S. Temel, “Flying Ad-Hoc Networks (FANETs): A Survey,” Ad Hoc Networks, vol. 11, no. 3, pp. 1254–1270, 2013.
  5. P. Corke, R. Peterson, and D. Rus, “Networked Robots: Flying Robot Navigation Using a Sensor Net,” in Proc. 11th Int. Symp. Robotics Research, 2003.
  6. H. Menouar, I. Güvenc, K. Akkaya, A. S. Uluagac, A. Kadri, and A. Tuncer, “UAV-Enabled Intelligent Transportation Systems for the Smart City: Applications and Challenges,” IEEE Communications Magazine, vol. 55, no. 3, pp. 22–28, 2017.
  7. J. Valente, D. Sanz, A. Barrientos, J. Del Cerro, and A. Ribeiro, “An Air-Ground Wireless Sensor Network for Crop Monitoring,” Sensors, vol. 11, no. 6, pp. 6088–6108, 2011.
  8. K. P. Valavanis and G. J. Vachtsevanos, Handbook of Unmanned Aerial Vehicles. Dordrecht, Netherlands: Springer, 2015.
  9. A. Ollero and I. Maza, “Multiple UAV Cooperative Systems for Remote Sensing and Surveillance Missions,” in Proc. Int. Conf. Unmanned Aircraft Systems, 2007.
  10. B. R. Mahafza, Introduction to Unmanned Aircraft Systems. Boca Raton, FL, USA: CRC Press, 2014.
  11. M. Erdelj, M. Król, and E. Natalizio, “Wireless Sensor Networks and Multi-UAV Systems for Natural Disaster Management,” Computer Networks, vol. 124, pp. 72–86, 2017.
  12. D. Floreano and R. J. Wood, “Science, Technology and the Future of Small Autonomous Drones,” Nature, vol. 521, pp. 460–466, 2015.
  13. M. Dorling, J. Heinrichs, G. G. Messier, and S. Magierowski, “Vehicle Routing Problems for Drone Delivery,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 1, pp. 70–85, Jan. 2017.
  14. L. Merino, F. Caballero, J. R. Martínez-de-Dios, I. Maza, and A. Ollero, “An Unmanned Aircraft System for Automatic Forest Fire Monitoring and Measurement,” Journal of Intelligent & Robotic Systems, vol. 65, nos. 1–4, pp. 533–548, 2012.
  15. A. R. Girard, A. S. Howell, and J. K. Hedrick, “Border Patrol and Surveillance Missions Using Multiple Unmanned Air Vehicles,” in Proc. IEEE Conf. Decision and Control, 2004.
  16. F. Nex and F. Remondino, “UAV for 3D Mapping Applications: A Review,” Applied Geomatics, vol. 6, no. 1, pp. 1–15, 2014.
  17. H. D. Yoo and S. M. Chankov, “Drone-Delivery Using Autonomous Mobility: An Innovative Approach to Future Last-Mile Delivery Problems,” in Proc. IEEE Int. Conf. Industrial Engineering and Engineering Management (IEEM), Bangkok, Thailand, 2018, pp. 1216–1220, doi:10.1109/IEEM.2018.8607829.
  18. A. W. Talab, “Improving Communication Protocols Based on Crucial Data Transmission Security for IoT Techniques,” International Journal of Computational and Electronic Aspects in Engineering, vol. 6, no. 1, pp. 1–11, Mar. 2025.
  19. H. G. Ayoub et al., “Efficient Real-Time Key Generation for IoT Using Multidimensional Chaotic Maps,” International Journal of Computational and Electronic Aspects in Engineering, vol. 6, no. 1, pp. 24–34, Mar. 2025.
  20. O. A. Abdaumran, “IoT-Enabled Real-Time Fault Monitoring in Overhead Power Lines Using ESP32 and Arduino Integrated Development,” International Journal of Computational and Electronic Aspects in Engineering, vol. 6, no. 3, pp. 190–201, Aug. 2025.
  21. A. Sharma, P. Kumar, and R. Mishra, “Lightweight ECC-Based Hybrid Encryption for Resource-Constrained IoT Networks,” International Journal of Computational and Electronic Aspects in Engineering, vol. 5, no. 2, pp. 45–53, 2024.
  22. M. Fatima, S. Ahmed, and K. Reddy, “Post-Quantum Hybrid Cryptography for Secure-Cloud Continuum,” International Journal of Computational and Electronic Aspects in Engineering, vol. 6, no. 1, pp. 12–21, 2025.
  23. V. Patel, N. Desai, and S. Kaur, “Blockchain-Enhanced Security Framework for IoT-Fog Networks: A Performance Analysis,” International Journal of Computational and Electronic Aspects in Engineering, vol. 6, no. 3, pp. 78–89, 2025.
  24. R. Kumar, A. Singh, and P. Gupta, “An Optimized RSA-ElGamal Hybrid Cryptosystem for Smart Healthcare IoT Applications,” International Journal of Computational and Electronic Aspects in Engineering, vol. 7, no. 1, pp. 22–31, 2026.
  25. H. N. Jumaah, “Embedded Reversibility Data in an Encrypted Photograph: A Case Study,” International Journal of Computational and Electronic Aspects in Engineering, vol. 5, no. 3, pp. 73–79, Sep. 2024.
  26. N. K. Younis, M. H. Qahtan, and M. R. Ahmed, “A Review of Artificial Intelligence Techniques for Medical Image Enhancement,” International Journal of Computational and Electronic Aspects in Engineering, vol. 6, no. 2, pp. 98–107, Jun. 2025.
  27. Y. M. Abdall, “AI-Driven Privacy Shield: A Learning Framework,” International Journal of Computational and Electronic Aspects in Engineering, vol. 6, no. 3, pp. 180–189, 2025.