Development of Temperature Preference Learning System to Optimize the Energy Consumption in the Building

Abstract

It is undeniable that as technology advances, robots will slowly start taking over the more labour intensive jobs hence pushing the human workforce from labour intensive work into a more intellectual intensive work. This shift in workforce also means the usage of office spaces where a multitude of cooling appliances are used to maintain the overall temperature of the office however, it is simply energy inefficient as well as expensive to leave all the appliances on all the time as the amount of people vary and so too does the need for said appliances to be on throughout the day. This work addresses the problems mentioned above by implementing an environmental preference learning algorithm coupled with a system which can be deployed to control and received feedback of any electrical appliances from a distance via Bluetooth. For this purpose, we used the appliance preference patterns exhibit by people corresponding to the room temperature as well as number of people in the room the time as the learning data and subsequently use it to determine what appliance should be on or off at any given time and condition so that the preferred room temperature as well as optimum room comfort level can be achieved.