EAGER: User-Centric Interdependent Urban Systems: Using Multi-Modal Transportation Data for Demand Prediction and Management in Buildings
Team: Sean Qian (PI), Xuesong Liu (Co-PI, CMU), Mario Berges (Co-PI, CMU)
Funding source: National Science Foundation
Start/End time: 2016-2018
​
We propose an approach that utilizes processed probe request log dataset collected through Wi-Fi network for occupancy estimation and HVAC system control. Occupancy detection approaches are explored and compared to illustrate that the WiFibased approach fit the requirements of HVAC controls. WiFi-based occupancy detection and prediction approaches are investigated using a real-world case study, and an integrated framework for occupancy-based predictive HVAC optimization is proposed for future application.
Publication
-
Xuan Li, Pine Liu, Sean Qian (2017), “Towards An Occupancy-enhanced Building HVAC Control Strategy Using Wi-Fi Probe Request Information”, Proceedings of the 2017 International Workshop on Computing in Civil Engineering. [URL]
Award: NSF Award page