Syracuse University

Bing Dong

Jianshun Zhang

An HVAC control system having a cloud-based optimization engine in communication with a local building hub that interfaces with the building HVAC system and room units. The cloud-based optimization engine implements an optimal and predictive control strategy to integrate occupancy prediction, weather forecasting, and modeling of indoor infection risk, indoor air quality, and building energy consumptions. The control strategy includes a model-based predictive control and a model-free reinforcement learning approach. The control strategy considers outdoor weather (both thermal and air quality) conditions, indoor occupancy and requirements for IAQ and infectious risk reduction to decide whether outdoor air should be introduced and how much fresh air will be introduced into the space. Communications with the building hub allow the local HVAC unit to be driven according to the optimization plan. Individual room sensing units can provide local sensor data to the cloud-based optimization engine.

Control system for medium-sized commercial buildings

A system and method for minimizing indoor infection risk and improving indoor air quality (IAQ) while maximizing energy savings. The system integrates occupancy detection and forecasting, outdoor weather conditions and forecasting, indoor infection risks and air quality modeling, any tunable air filtration, the clean air delivery rate, and any portable air cleaners. The system outputs the total amount of outdoor air intake, the air temperature of the supply air into the space, the supply air flow rate into the space, the operation mode of tunable air filtration/purification/disinfection, the operation mode of the in-room air cleaner, and space/room temperature set-points, and thus can serve as the central controller for an HVAC system.

Growing community of inventors

Manlius, NY, United States of America

Bing Dong