Investigation of Power Consumption of Refrigeration Model and Its Exploratory Data Analysis (EDA) by Using Machine Learning (ML) Algorithm

HVAC (Heating Ventilation and Air-conditioning) play a vital role in various sectors, from residential and commercial to industrial applications. Understanding and optimizing the power consumption of these systems is crucial for energy efficiency and cost savings. This research aims to explore the power consumption of refrigeration systems during power ON mode and perform Exploratory Data Analysis (EDA) and Machine Learning (ML) algorithms to gain insights into factors influencing power consumption. The experimentation is conducted on refrigeration test rig and performance is calculated during power ON mode by adding NPCM (Nano-Phase Change Material) in evaporator section and comparison is to be made without implementation of NPCM in evaporator section. By utilizing ML algorithms, it becomes possible to create predictive models that can assist in optimizing the power consumption of refrigeration systems, reducing energy costs, and minimizing environmental impact. The accuracy of model by linear regression is around 66% by implementation NPCM in refrigeration system where as 23% model accuracy is found without implementation of NPCM in refrigeration system. Also it is observed that coefficient of performance of refrigeration system increase by around 15 to 18% as compared with without use of NPCM. Also power consumption is reduces to 5 to 7% with implementation of nano phase change material in refrigeration system.