Abstract
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.
Original language | English (US) |
---|---|
Title of host publication | ICNSC 2016 - 13th IEEE International Conference on Networking, Sensing and Control |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781467399753 |
DOIs | |
State | Published - May 25 2016 |
Event | 13th IEEE International Conference on Networking, Sensing and Control, ICNSC 2016 - Mexico City, Mexico Duration: Apr 28 2016 → Apr 30 2016 |
Other
Other | 13th IEEE International Conference on Networking, Sensing and Control, ICNSC 2016 |
---|---|
Country/Territory | Mexico |
City | Mexico City |
Period | 4/28/16 → 4/30/16 |
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Control and Systems Engineering
- Modeling and Simulation