Seminar 41 — Optimizing Smart Building Operation with Machine Learning
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.
This advanced session discusses sophisticated machine learning (ML) optimization techniques and their application to improve operational efficiency and delivery of services for complex systems such as buildings and campuses. This session includes three presentations that focus on Supervised Learning and Reinforced Learning techniques for driving building controls towards superior strategies.
- A Comparison Study of ASHRAE Guideline 36 Supervisory Controls and Deep Reinforcement Learning-Based Controller for a Multi-Zone VAV System< /br> Zheng O’Neill, Ph.D., P.E., Fellow ASHRAE, Texas A&M University, College Station, TX
- Implementation of Structured Reinforcement Learning for Supply Air Temperature Control< /br> Amanda Pertzborn, Ph.D., Associate Member, NIST, Gaithersburg, MD
- Supervised Learning: A Powerful Tool for Smart Building Optimization< /br> Omar Abdelaziz, Member, Zewail City of Science and Technology, Giza, Egypt
Product Details
- Published:
- 2023
- Units of Measure:
- Dual
- File Size:
- 1 file , 87 MB
- Product Code(s):
- D-TO22Sem41
Seminar 41 — Optimizing Smart Building Operation with Machine Learning
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.
This advanced session discusses sophisticated machine learning (ML) optimization techniques and their application to improve operational efficiency and delivery of services for complex systems such as buildings and campuses. This session includes three presentations that focus on Supervised Learning and Reinforced Learning techniques for driving building controls towards superior strategies.
- A Comparison Study of ASHRAE Guideline 36 Supervisory Controls and Deep Reinforcement Learning-Based Controller for a Multi-Zone VAV System
Zheng O’Neill, Ph.D., P.E., Fellow ASHRAE, Texas A&M University, College Station, TX
- Implementation of Structured Reinforcement Learning for Supply Air Temperature Control
Amanda Pertzborn, Ph.D., Associate Member, NIST, Gaithersburg, MD
- Supervised Learning: A Powerful Tool for Smart Building Optimization
Omar Abdelaziz, Member, Zewail City of Science and Technology, Giza, Egypt
Product Details
- Published:
- 2022
- Units of Measure:
- Dual
- File Size:
- 1 file
- Product Code(s):
- D-TO22Sem-41
- Note:
- This product is unavailable in Russia, Belarus
