Abstract
An interior partition wall integrated with active thermal storage and a dynamic insulation system was built and then installed in an office building in Oak Ridge, Tennessee, TN. This smart wall, termed the Empower Wall, was equipped with embedded pipes in the building envelope core component and an additional pipe network enclosing rigid insulation to switch on and off the active insulation dynamically. The performance of the wall's contribution to cooling load reduction under different parameters has been investigated in previous publications. Aiming to be deployed into model predictive control and other optimization methods, simplified and reliable models for the developed wall and the room accommodating it are required. They are needed to characterize the properties and thermal response of both Empower Wall and building envelope, which form an essential component for accurate indoor temperature or cooling/heating demand prediction. In this study, simplified gray-box and regression models as well as machine learning model were developed and the performance of them were compared and analyzed.
| Original language | English |
|---|---|
| Pages (from-to) | 609-618 |
| Number of pages | 10 |
| Journal | Proceedings of the Thermal and Fluids Engineering Summer Conference |
| Volume | 2023-March |
| State | Published - 2023 |
| Event | 8th Thermal and Fluids Engineering Conference, TFEC 2023 - Hybrid, College Park, United States Duration: Mar 26 2023 → Mar 29 2023 |
Funding
This material is based upon work supported by the US Department of Energy’s (DOE’s) Office of Federal Energy Management Program (FEMP). This research used resources of Oak Ridge National Laboratory’s Building Technologies Research and Integration Center, which is a DOE Office of Science User Facility. This work was funded under DOE FEMP activity no. EL1710000. This manuscript has been authored by UT-Battelle LLC under contract DEAC05-00OR22725 with DOE. The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. This material is based upon work supported by the US Department of Energy's (DOE's) Office of Federal Energy Management Program (FEMP). This research used resources of Oak Ridge National Laboratory's Building Technologies Research and Integration Center, which is a DOE Office of Science User Facility. This work was funded under DOE FEMP activity no. EL1710000. This manuscript has been authored by UTBattelle LLC under contract DEAC05-00OR22725 with DOE. The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes.
| Funders | Funder number |
|---|---|
| U.S. Department of Energy | EL1710000, DEAC05-00OR22725 |
| Office of Science | |
| UT-Battelle |
Keywords
- Active insulation system
- Gray-box modeling
- Machine learning algorithm
- Thermal energy storage system