Adithya SM

Temperature prediction in cooling systems is a
significant theme within the application domain of IoT and
Machine Learning. It is tough to vary the temperature during
sleeping hours, meeting times, and for the most part, wherever
an arbitrarily large number of people occupy a room. There
are several parameters that play a pivotal role in deciding
the accuracy of temperature predictions made by automatic
systems, which include the quality and quantity of the data
used to train the machine learning model, the complexity of
the model, and the variability of the environment in which the
air conditioner is used. Automatic temperature prediction also
improves comfort levels for building occupants, and reduction of energy consumption as the air condi-
tioner is set to maintain the predicted comfortable temperature
without requiring manual adjustments. The proposed Intelligent
Air Conditioning system can predict the desired
temperature of the user and self-adjust the predicted temperature
of the room automatically using IoT and Machine Learning
techniques. The algorithm deployed in the system inputs various
physical parameters such as humidity, AQI, altitude, atmospheric
pressure, number of occupants, room dimensions, ambient light,
light intensity, and air velocity to ensure accurate temperature
predictions. If the predicted temperature does not match the
comfort temperature recommended by ASHRAE, the system will
cool/condition the room with a temperature within the range of
comfort temperature recommended by ASHRAE and close to
the predicted temperature. The ceiling fan unit is employed to
compensate for the temperature difference without decreasing the
room temperature using differential air velocity. This promising
technology will satisfy the complete level of automation through
Image processing, Machine Learning, and IoT.


The system is capable of reducing the energy consumption upto 63 percentage,thus upholding the idea of sustainability and reducing carbon footprints.