Seminar 27 — Bayesian Optimization for Building and Equipment Applications
Click here to purchase
This product is a zip file that contains files that consist of PowerPoint slides synchronized with the audio-recording of the speaker, PDF files of the slides, and audio only (mp3 format) as noted.
Bayesian optimization is an approach for optimizing complex systems which has attracted recent interest because of its ability to efficiently solve computationally expensive problems. This seminar introduces the fundamentals of Bayesian optimization to illustrate the benefits of this approach, and describe its application to a wide range of problems, including chiller plant design and control, simultaneous model calibration of both buildings and equipment, and the tuning of equipment control parameters to get optimal installed system performance.
- Bayesian Optimization: An Efficient Approach for Designing and Assessing Building Systems< /br> Veronica Adetola, Ph.D., Member, Pacific Northwest National Laboratory, Richland, WA
- Meta-Learned Few-Shot Bayesian Optimization for Calibrating Physics: Informed Models of Coupled Building and HVAC Dynamics< /br> Ankush Chakrabarty, Ph.D., Mitsubishi Electric Research Laboratories, Cambridge, MA
- MPC Tuning with Bayesian Optimization: A Case Study for HVAC Central Plants< /br> Qiugang Lu, Ph.D., Texas Tech University, Lubbock, TX
Product Details
- Published:
- 2023
- Units of Measure:
- Dual
- File Size:
- 1 file , 82 MB
- Product Code(s):
- D-TO22Sem27
Seminar 27 — Bayesian Optimization for Building and Equipment Applications
Click here to purchase
This product is a zip file that contains files that consist of PowerPoint slides synchronized with the audio-recording of the speaker, PDF files of the slides, and audio only (mp3 format) as noted.
Bayesian optimization is an approach for optimizing complex systems which has attracted recent interest because of its ability to efficiently solve computationally expensive problems. This seminar introduces the fundamentals of Bayesian optimization to illustrate the benefits of this approach, and describe its application to a wide range of problems, including chiller plant design and control, simultaneous model calibration of both buildings and equipment, and the tuning of equipment control parameters to get optimal installed system performance.
- Bayesian Optimization: An Efficient Approach for Designing and Assessing Building Systems
Veronica Adetola, Ph.D., Member, Pacific Northwest National Laboratory, Richland, WA
- Meta-Learned Few-Shot Bayesian Optimization for Calibrating Physics: Informed Models of Coupled Building and HVAC Dynamics
Ankush Chakrabarty, Ph.D., Mitsubishi Electric Research Laboratories, Cambridge, MA
- MPC Tuning with Bayesian Optimization: A Case Study for HVAC Central Plants
Qiugang Lu, Ph.D., Texas Tech University, Lubbock, TX
Product Details
- Published:
- 2022
- Units of Measure:
- Dual
- File Size:
- 1 file
- Product Code(s):
- D-TO22Sem-27
- Note:
- This product is unavailable in Russia, Belarus
