Intelligent and adaptive temperature control for large-scale buildings and homes

Temperature control in smart buildings and homes can be automated by having computer controlled air-conditioning systems along with temperature sensors that are distributed in the controlled area. However, programming actuators in large-scale buildings and homes can be time consuming and expensive. We present an approach that algorithmically sets up the control system that can generate optimal actuator settings for large-scale environments. This paper clearly describes how the temperature control problem is modeled using convex quadratic programming. The impact of every air conditioner(AC) on each sensor at a particular time is learnt using linear regression model. The resulting system controls air-conditioning equipments to ensure the maintenance of user comforts and low cost of energy consumptions. Our method works as generic control algorithms and are not preprogrammed for a particular place. The system can be deployed in large scale environments. It can accept multiple target setpoints at a time, which improves the flexibility and efficiency for temperature control. The feasibility, adaptivity and scalability features of the system have been validated through various actual and simulated experiments.