[CAISS] Green Building: Model Predictive Control For Building Energy Systems08/24/2013 - 08/24/2013


Time: 8/24/2013 
1:30 pm -2:30pm 

Venue: LSI headquarter
1320 Ridder Park Dr
San Jose, CA 95131

 Please register here for ticket


Speaker: Dr. Yudong Ma,   Brightbox Technologies

BrightBox Technologies, a Bay Area based start-up, develops and markets advanced HVAC controls to large commercial buildings to improve performance and comfort while significantly reducing operating costs.   While most other energy efficiency software companies are focusing on monitoring and/or fault detection in buildings, the BrightBox platform provides actual control and real-time, ongoing system optimization, along with diagnostics and verification of results. 
Yudong is one of the key persons that responsible for the R&D of BrightBox Technologies.


The building sector consumes about 40% of energy used in the United States and is responsible for nearly 40% of greenhouse gas emissions. Energy reduction in this sector by means of cost-effective and scalable approaches will have an enormous economic, social, and environmental impact. Achieving substantial energy reduction in buildings may require to rethink the entire processes of design, construction, and operation of buildings.  

Model Predictive Control (MPC) is the only control methodology that can systematically take into account future predictions during the control design stage while satisfying the system operating constraints. This talk focuses on the design and implementation of MPC for building cooling and heating systems. 

The objective is to develop a control methodology that can 1) reduce building energy consumption while maintaining indoor thermal comfort by using predictive knowledge of occupancy loads and weather information, (2) easily and systematically take into account the presence of storage devices, demand response signals from the grid, and occupants feedback, (3) be implemented on existing inexpensive and distributed building control platform in real-time, and (4) handle model uncertainties and prediction errors both at the design and implementation stage.