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A Machine Learning Approach to Predict the Performance of Refrigerator and Air Conditioning Using Gaussian Process Regression and Support Vector Methods

Author(s):

Harinarayan Sharma, Sonam Kumari, Aniket K. Dutt, Pawan Kumar* and Mamookho E. Makhatha   Pages 1 - 8 ( 8 )

Abstract:


Aim: Develop machine learning models for the performance of refrigerator and airconditioning system.

Background: The Coefficient Of Performance (COP) of Refrigerator and Air-Conditioning (RAC) is a complex function of evaporative temperature and concentration of nano-particle in lubricants. In recent years, researchers focus on experimental study for improvement of COP. Further, few researchers applied simulation techniques such as fuzzy system, Artificial Neural Network (ANN), simulated annealing, etc. to the Vapour Compression Refrigeration (VCR) cycle. There is a scarcity of modeling research work for the performance of RAC system.

Objective: The study aims to develop the machine learning predictive models for the performance of refrigerator and air-conditioning system using experimental data.

Methods: The experiment was performed on VCR system to determine COP. Three different concentration of lubricants (added 0.5, 1.0 and 1.5g nano-TiO2 particle on 1 liter of Polyolester (POE) oil) were used. The experimentally calculated COP was used to train and test the machine learning models. Gaussian Process Regression (GPR) and Support Vector Regression (SVR) methods were applied to develop the models.

Results: The experimental result reveals that the COP increases with increasing the concentration (of nano particles) at a given temperature. The addition of 0.5 and 1.0g TiO2 in the POE oil shows better rate of increment in the COP in comparison to addition of 1.5g TiO2 in the POE oil. Machine learning models using GPR and SVR with RBF kernel function is the most appropriate machine learning model for the nonlinear relationship between the output parameter (COP) and the input parameter (evaporative temperature and concentration of TiO2).

Conclusion: The present study was conducted to investigate the machine learning approaches for performance of RAC system using experimental data sets. The experimental result shows that R134a and TiO2-POE nanolubricant work efficiently and the coefficient of performance of VCR system increases with concentration of nano-particle. The developed model performance is compared using coefficient of correlation and RSME values. After comparison, it is concluded that RBF based GPR model is the best fit machine learning model to predict the COP in the context of any other model for this data set.

Keywords:

Coefficient of performance, Gaussian process, kernel functions, machine learning, nano-lubricants, refrigeration and air-conditioning, support vector.

Affiliation:

Department of Mechanical Engineering, Netaji Subhas Institute of Technology, Bihta, Bihar, Department of Computer Science and Engineering, Netaji Subhas Institute of Technology, Bihta, Bihar, Department of Mechanical Engineering, Netaji Subhas Institute of Technology, Bihta, Bihar, Department of Engineering Metallurgy, Faculty of Engineering and the Built Environment, University of Johannesburg, John Orr Building, DFC, 25 Louisa St, Doornfontein, Johannesburg, 2028, Department of Engineering Metallurgy, Faculty of Engineering and the Built Environment, University of Johannesburg, John Orr Building, DFC, 25 Louisa St, Doornfontein, Johannesburg, 2028



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