Parametric and Predictive Analysis of Window AC System Based on VCRS
DOI:
https://doi.org/10.69996/ijari.2025003Keywords:
Coefficient of Performance (COP), R2 (R squared), Machine learning, Mean Squared Error, Ridge regressionAbstract
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.
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