Monday, September 13, 2010
PhD Dissertation Defense Presentation
Bing Dong, PhD Candidate, Center for Building Performance and DiagnosticsMonday, September 20, 2010
Time: 2:00 pm to 5:00 pm
Location: MMC 108; Dean's Conference Room
Abstract: Integrated Building Heating, Cooling and Ventilation Control
Current research studies show that building heating, cooling and ventilation energy consumption accounts for nearly 40% of the total building energy use in the U.S. The potentials for saving energy through building control systems vary from 5% to 20% based on recent market surveys. In addition, building control affects environmental performances such as thermal, visual, air quality, etc. and occupancy such as working productivity and comfort.
Building control has been proven to be important both in design and operation stages.
Both building control design and operation need consistent and reliable static and dynamic information from various resources. Static information includes building geometry, construction and HVAC equipments. Dynamic information includes zone environmental performance, occupancy and outside weather information during operation stage. At the same time, model-based predicted control can help optimize energy use while maintaining indoor set-point temperature when occupied. Unfortunately, several issues in the current approach of building control design and operation impede this goal. These issues include: a) dynamic information data are not readily available such as real-time on-site weather (e.g., temperature, wind speed and solar radiation) and occupancy (number of occupants and occupancy duration in the space); b) a comprehensive whole building energy model is not fully integrated into advanced controls for accuracy and robustness; c) real-time implementation cases of such control on indoor air temperature are few. This
dissertation aims to investigate and solve these issues based on an integrated building control approach.
This dissertation introduces and illustrates a novel methodology for integrated building heating, cooling and ventilation control with the objective to reduce energy consumption and maintain indoor temperature set-point, based on the prediction of occupant behavior patterns and weather conditions. Several advanced machine learning methods such as Adaptive Gaussian Process, Hidden Markov Model, Episode Discovery and Semi Markov Model were modified and implemented into this dissertation. A non-linear Model Predictive Control (NMPC) was designed and implemented in real-time based on Dynamic Programming. The experiment test-bed was setup in the Solar Decathlon House (2005), with over 100 sensor points measuring indoor
environmental parameters such as temperature, relative humidity, CO2, lighting, motion and acoustics, and the power consumption for electrical plugs, HVAC and lighting. The outdoor environmental parameters such as
temperature, relative humidity, CO2, global horizontal solar radiation and wind speed are measured by the on-site weather station. The designed controller was implemented through LabView.
The experiments were carried out for two continuous months in the heating season and for a week in cooling season. The results show that there is a 26% measured energy reduction in heating season compared with the scheduled temperature set-points, and 17.8% energy reduction in cooling season. The further simulation-based results show that with tighter building façade, the cooling energy reduction could reach 20%. Overall, the heating, cooling and ventilation energy reduction could reach nearly 50% based on this novel integrated control approach for the entire heating/cooling testing periods compared to the conventional scheduled temperature set-point.