Description: For personalized comfort, studies proposed to use personal environmental control systems (PECs), especially in shared spaces. Unlike conventional centrally-controlled HVAC system, where people share the same set point temperature, PEC systems can meet the comfort requirements of all occupants, albeit at the cost of additional energy expenditure.
Therefore, we first proposed a system that combines PEC with model predictive control to further minimize HVAC energy consumption and maximize user comfort. Though advanced predictive control strategies (such as MPC) have the potential to optimize HVAC operations significantly, to the best of our knowledge, there exists no study that quantifies the influence of the prediction errors (especially in occupancy) on the energy consumption of HVAC and on the occupants’ comfort.
In this work, we also developed a building thermal simulator to systematically investigate the impact of occupancy prediction errors on occupant comfort and energy consumption. Our analysis shows that in our test building, as occupancy prediction error increases from 5% to 20% the performance of an MPC-based HVAC controller becomes worse than that of even a simple static schedule. However, when combined with a personal environmental control (PEC) system, HVAC controllers are considerably more robust to prediction errors.
Technologies: C++, MATLAB, Python, AMPL
Teammates: Dr. Rachel K. Kalaimani, Prof. Srinivasan Keshav, Prof. Catherine Rosenberg
Publications:
- Milan Jain, Rachel K. Kalaimani, Srinivasan Keshav, Catherine Rosenberg; “Using Personal Environmental Comfort Systems to Mitigate the Impact of Occupancy Prediction Errors on HVAC Performance”, in Energy Informatics Journal (2018).
