Modelling and control of HVAC systems
Nearly 50 percent of Singapore's electricity consumption comes from buildings, both residential and non-residential. Commercial buildings, such as shopping malls, hotels, hospitals, and offices are the biggest culprits. Current HVAC systems are not optimally operated to enhance building energy...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/157641 |
| _version_ | 1824453479934984192 |
|---|---|
| author | Men, Bunnaroth |
| author2 | Cai Wenjian |
| author_facet | Cai Wenjian Men, Bunnaroth |
| author_sort | Men, Bunnaroth |
| collection | NTU |
| description | Nearly 50 percent of Singapore's electricity consumption comes from buildings, both residential and non-residential. Commercial buildings, such as shopping malls, hotels, hospitals, and offices are the biggest culprits. Current HVAC systems are not optimally operated to enhance building energy performance and consumption. Advances in technology and their influence on the development of novel control techniques for HVAC systems have increased their energy efficiency. However, the process of running HVAC equipment in buildings is frequently overlooked, even though it has the potential to significantly improve the energy efficiency of the system.
Air balancing is one of important factor in the HVAC system to ensure sufficient amount of air is distributed to all building occupants to prevent over-ventilation or under-ventilations. Therefore, it is really important to have proper air balancing the ventilation system to provide optimal comfort and the same time energy savings.
In this research, the author will leverage on the use of neural network for air balancing in the HVAC system and a non-iterative air balancing will be introduced. The data of the air balancing model will be collected in a verified testbed at EEE – ERI@N Joint Lab (S2.2 B4-03) via MODBUS protocol on MATLAB. Performance evaluation of the training algorithm will be evaluated in determine the terminal damper angle. |
| first_indexed | 2025-02-19T03:07:05Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/157641 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:07:05Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1576412023-07-07T18:58:30Z Modelling and control of HVAC systems Men, Bunnaroth Cai Wenjian Wang Youyi School of Electrical and Electronic Engineering ewjcai@ntu.edu.sg, EYYWANG@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Nearly 50 percent of Singapore's electricity consumption comes from buildings, both residential and non-residential. Commercial buildings, such as shopping malls, hotels, hospitals, and offices are the biggest culprits. Current HVAC systems are not optimally operated to enhance building energy performance and consumption. Advances in technology and their influence on the development of novel control techniques for HVAC systems have increased their energy efficiency. However, the process of running HVAC equipment in buildings is frequently overlooked, even though it has the potential to significantly improve the energy efficiency of the system. Air balancing is one of important factor in the HVAC system to ensure sufficient amount of air is distributed to all building occupants to prevent over-ventilation or under-ventilations. Therefore, it is really important to have proper air balancing the ventilation system to provide optimal comfort and the same time energy savings. In this research, the author will leverage on the use of neural network for air balancing in the HVAC system and a non-iterative air balancing will be introduced. The data of the air balancing model will be collected in a verified testbed at EEE – ERI@N Joint Lab (S2.2 B4-03) via MODBUS protocol on MATLAB. Performance evaluation of the training algorithm will be evaluated in determine the terminal damper angle. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-21T11:38:00Z 2022-05-21T11:38:00Z 2022 Final Year Project (FYP) Men, B. (2022). Modelling and control of HVAC systems. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157641 https://hdl.handle.net/10356/157641 en A1156-211 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Men, Bunnaroth Modelling and control of HVAC systems |
| title | Modelling and control of HVAC systems |
| title_full | Modelling and control of HVAC systems |
| title_fullStr | Modelling and control of HVAC systems |
| title_full_unstemmed | Modelling and control of HVAC systems |
| title_short | Modelling and control of HVAC systems |
| title_sort | modelling and control of hvac systems |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | https://hdl.handle.net/10356/157641 |
| work_keys_str_mv | AT menbunnaroth modellingandcontrolofhvacsystems |