Parametric and Predictive Analysis of Window AC System Based on VCRS

Authors

  • Deshdeep Gambhir Assistant Professor, Department of Mechanical Engineering, Maharaja Agrasen Institute of Technology, PSP Area, Plot No. 1, Sector 22, Rohini, New Delhi, Delhi - 110086, India
  • Ashish Raj Student, , Department of Mechanical Engineering, Maharaja Agrasen Institute of Technology, PSP Area, Plot No. 1, Sector 22, Rohini, New Delhi, Delhi - 110086, India.

DOI:

https://doi.org/10.51976/w4ctwr78

Keywords:

Coefficient of Performance (COP), Machine learning, Mean Squared Error, Ridge regression, R2 (R squared), VCRS

Abstract

This research examines the performance of a modified window air conditioning system. The setup allows independent control of the condenser fan and blower. Experiments were conducted to study the effects of condenser and evaporator pressures on system efficiency. The Coefficient of Performance (COP) was the key performance metric. Data collected from the experiments were used to train machine learning models. Ridge regression techniques were applied to predict system performance. Results show a strong link between pressure parameters and COP. The study highlights how combining experimental insights with computational tools can improve HVAC system efficiency. Future work may explore real-time optimization methods to enhance cooling performance further.

References

[1] S. Kumar, P. Singh and A. Sharma, “Performance Analysis of Vapour Compression Refrigeration System with Variable Condenser Pressure,” International Journal of Refrigeration Systems, 35(2), 2018, 120–128.

[2] V.K. Rao, T. Patel and N. Desai, “Impact of Fan Speeds and Heat Transfer Coefficients on COP in Air Conditioning Systems,” Energy and Buildings, 78(4), 2021, 456–468.

[3] L. Gupta, S. Verma and Z. Khan, “Application of Lasso and Ridge Regression in Predictive Modeling of Refrigeration Systems,” International Journal of Energy Studies, 18(1), 2022, 201–212

[4] G. James, D. Witten, T. Hastie and R. Tibshirani, “An Introduction to Statistical Learning with Applications in R,” Springer Publication, 2013.

[5] T. Nguyen and C. Lee, “Experimental and Computational Analysis of Window Air Conditioners for Energy Optimization,” HVAC Research Journal, 42(5), 2021, 189–202.

[6] J. Nielsen and A. Thompson, “Regularization Techniques for Linear Regression Models: A Comparative Study,” Computational Methods in Mechanical Engineering, 10(2), 2018, 75–85.

Published

2025-03-31

Issue

Vol. 13 No. 01 (2025): Volume: 13 Issue: 1

Section

Research Article

License

Copyright (c) 2025 International Journal of Advance Research and Innovation(IJARI, 2347-3258)

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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How to Cite

Deshdeep Gambhir, & Ashish Raj. (2025). Parametric and Predictive Analysis of Window AC System Based on VCRS. International Journal of Advance Research and Innovation(IJARI, 2347-3258), 13(01). https://doi.org/10.51976/w4ctwr78