A data-driven thermal sensation model based predictive controller for indoor thermal comfort and energy optimization

Xiao Chen, Qian Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationDynamic 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
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791846193
DOIs
StatePublished - Jan 1 2014
EventASME 2014 Dynamic Systems and Control Conference, DSCC 2014 - San Antonio, United States
Duration: Oct 22 2014Oct 24 2014

Publication series

NameASME 2014 Dynamic Systems and Control Conference, DSCC 2014
Volume2

Other

OtherASME 2014 Dynamic Systems and Control Conference, DSCC 2014
CountryUnited States
CitySan Antonio
Period10/22/1410/24/14

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All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

Cite this

Chen, X., & Wang, Q. (2014). A data-driven thermal sensation model based predictive controller for indoor thermal comfort and energy optimization. In 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 [6131] (ASME 2014 Dynamic Systems and Control Conference, DSCC 2014; Vol. 2). American Society of Mechanical Engineers. https://doi.org/10.1115/DSCC2014-6131