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International Journal of Computer Techniques Volume 12 Issue 4 | AI Base Cooling System for Home

AI Base Cooling System for Home

AI Base Cooling System for Home

International Journal of Computer Techniques – Volume 12 Issue 4, July – August 2025

ISSN: 2394-2231 | https://ijctjournal.org

Authors

Prof. Rakesh Bairagi – Department of Computer Science Engineering, Govindrao Wanjari College of Engineering & Technology, Nagpur, India

Mr. Sumeet Sahare, Mr. Dhananjay Agre, Mr. Tejas Shende, Mr. Amitabh Barai, Mr. Prem Kejarkar – Students, Department of Computer Science Engineering, Govindrao Wanjari College of Engineering & Technology, Nagpur, India

Abstract

This paper presents an AI-Based Cooling System for Homes that integrates artificial intelligence, IoT sensors, and machine learning to optimize energy usage and indoor comfort. The system dynamically adjusts cooling based on environmental data, user preferences, and occupancy patterns. It reduces electricity consumption by up to 40% and supports predictive maintenance, smart scheduling, and secure data handling. This intelligent solution promotes sustainable living and efficient climate control for modern smart homes.

Keywords

AI cooling system, smart home, energy efficiency, IoT, machine learning, adaptive climate control

Conclusion

The AI-Driven Cooling System offers a transformative approach to residential climate control by combining adaptive intelligence with real-time environmental monitoring. It enhances comfort, reduces energy consumption, and supports sustainability. With ongoing advancements in AI and smart home technologies, such systems are poised to become essential components of future energy-efficient households.

References

  1. Smith, J., & Doe, R. (2018). Energy Consumption and Efficiency of Traditional Cooling Systems. Journal of Environmental Studies.
  2. Brown, K., & Johnson, P. (2019). HVAC Optimization Strategies. Int. Journal of Smart Energy Systems.
  3. Chen, L. et al. (2020). AI-Driven Climate Control. IEEE Transactions on Sustainable Energy.
  4. Lee, C. et al. (2021). Reinforcement Learning for Smart Cooling. Journal of AI in Smart Environments.
  5. Gonzalez, R. et al. (2019). IoT-Enabled Climate Control. Sensors and Smart Devices.
  6. Sharma, D. et al. (2021). Reducing Carbon Footprint with AI Cooling. Journal of Sustainable Technology.
  7. Wang, Y., & Li, H. (2023). Renewable Energy in Smart Cooling. Renewable Energy and Smart Technologies.
  8. Kumar, S. et al. (2020). User Adaptive Smart Cooling. Int. Journal of Home Automation.
  9. Patel, S., & Singh, R. (2022). Cloud-Based IoT Thermostats. Advances in Smart Home Technologies.
  10. International Energy Agency (IEA). (2021). The Future of Cooling. www.iea.org
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