Integration of Artificial Intelligence in Refrigeration Systems: A Review

Abstract

The refrigeration industry is undergoing a transformative phase with the advent of Artificial Intelligence (AI) and data-driven control systems. Conventional refrigeration systems often rely on fixed control strategies, which can lead to inefficiencies in energy consumption, maintenance, and overall performance. The integration of AI techniques such as Machine Learning (ML), Deep Learning (DL), and Predictive Analytics is enabling intelligent automation, fault diagnosis, load forecasting, and energy optimization in both commercial and industrial refrigeration applications. AI-based models can analyze large datasets from sensors and IoT devices to predict system behavior, identify leaks, optimize compressor cycles, and maintain temperature stability with minimal human intervention. Moreover, reinforcement learning and neural network approaches are being developed to improve refrigerant flow control and adapt to varying environmental and load conditions dynamically.

Cite as:

V. Jamadar. (2025). Integration of Artificial Intelligence in Refrigeration Systems: A Review. Research and Reviews: Journal of Mechanics and Machines, 7(3), 9–17. 

https://doi.org/10.5281/zenodo.17838646