Comparatively Analysis of Compression Chiller (Model # GVWF 260) Using Machine Learning Techniques

A compression chiller is a type of refrigeration system used for cooling water or air. It uses mechanical compression to compress a refrigerant gas, which then flows through a condenser to release heat. This causes the refrigerant to condense into a high-pressure liquid that is put through an enhance valve to minimize pressure and temperature before being circulated through an evaporator to absorb heat. Compression chillers are commonly used in commercial and industrial settings for air conditioning, refrigeration, and process cooling. Recently, Several intelligent systems are increasingly being used with compression chiller systems to predict and monitor their performance. These systems use machine learning algorithms to analyze data from sensors installed throughout the chiller system to identify patterns and trends in its operation. By monitoring system performance in real-time and making adjustments to operating parameters based on predicted performance, these systems can optimize chiller efficiency and reduce energy consumption. This study highlights a comparative analysis of a Compression Chiller model # GVWF 260 using Machine Learning (ML) techniques. The goal of this research is to improve a predictive model that can accurately estimate the chiller's performance and energy consumption, and compare the results with the actual measurements. This model is able to predict the chiller's performance and energy consumption with a high degree of accuracy.