TY - GEN
T1 - A data-driven thermal sensation model based predictive controller for indoor thermal comfort and energy optimization
AU - Chen, Xiao
AU - Wang, Qian
N1 - Publisher Copyright:
Copyright © 2014 by ASME.
PY - 2014
Y1 - 2014
N2 - This paper proposes a model predictive controller (MPC) using a data-driven thermal sensation model for indoor thermal comfort and energy optimization. The uniqueness of this empirical thermal sensation model lies in that it uses feedback from occupants (occupant actual votes) to improve the accuracy of model prediction. We evaluated the performance of our controller by comparing it with other MPC controllers developed using the Predicted Mean Vote (PMV) model as thermal comfort index. The simulation results demonstrate that in general our controller achieves a comparable level of energy consumption and comfort while eases the computation demand posed by using the PMV model in the MPC formulation. It is also worth pointing out that since we assume that our controller receives occupant feedback (votes) on thermal comfort, we do not need to monitor the parameters such as relative humidity, air velocity, mean radiant temperature and occupant clothing level changes which are necessary in the computation of PMV index. Furthermore simulations show that in cases where occupants' actual sensation votes might deviate from the PMV predictions (i.e., a bias associated with PMV), our controller has the potential to outperform the PMV based MPC controller by providing a better indoor thermal comfort.
AB - This paper proposes a model predictive controller (MPC) using a data-driven thermal sensation model for indoor thermal comfort and energy optimization. The uniqueness of this empirical thermal sensation model lies in that it uses feedback from occupants (occupant actual votes) to improve the accuracy of model prediction. We evaluated the performance of our controller by comparing it with other MPC controllers developed using the Predicted Mean Vote (PMV) model as thermal comfort index. The simulation results demonstrate that in general our controller achieves a comparable level of energy consumption and comfort while eases the computation demand posed by using the PMV model in the MPC formulation. It is also worth pointing out that since we assume that our controller receives occupant feedback (votes) on thermal comfort, we do not need to monitor the parameters such as relative humidity, air velocity, mean radiant temperature and occupant clothing level changes which are necessary in the computation of PMV index. Furthermore simulations show that in cases where occupants' actual sensation votes might deviate from the PMV predictions (i.e., a bias associated with PMV), our controller has the potential to outperform the PMV based MPC controller by providing a better indoor thermal comfort.
UR - http://www.scopus.com/inward/record.url?scp=84929231017&partnerID=8YFLogxK
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U2 - 10.1115/DSCC2014-6131
DO - 10.1115/DSCC2014-6131
M3 - Conference contribution
AN - SCOPUS:84929231017
T3 - ASME 2014 Dynamic Systems and Control Conference, DSCC 2014
BT - Dynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization; Energy Storage, Optimization; Transportation and Grid Applications; Estimation and Identification Methods, Tracking, Detection, Alternative Propulsion Systems; Ground and Space Vehicle Dynamics; Intelligent Transportation Systems and Control; Energy Harvesting; Modeling and Control for Thermo-Fluid Applications, IC Engines, Manufacturing
PB - American Society of Mechanical Engineers
T2 - ASME 2014 Dynamic Systems and Control Conference, DSCC 2014
Y2 - 22 October 2014 through 24 October 2014
ER -
