A simple early warning system for air conditioners using machine learning to minimise maintenance costs and environmental impact

In hot climate countries, a high proportion of energy produced is used up for air conditioning. Since homeowners cannot always recognize when their air conditioners are losing efficiency, several faults remain undetected until catastrophic failure occurs. Here, a low-cost n early warning system is presented that only measures the temperatures at key locations of the refrigeration circuit along with the utilization rate of the system. A neural network is trained to recognize early signs of failure, and the trained model is small enough to fit in a microcontroller. Such a system can help not only with preventing excessive energy consumption prior to complete failure, but can also reduce the amount of refrigerant leaked into the atmosphere.