Machine learning-based model predictive control for ACMV system using edge device
The Building Management System (BMS) can monitor and manage a facility's mechanical, electrical, and electromechanical services. One subset of this system is Air-conditioning and Mechanical Ventilation (ACMV), which regulates interior temperatures. Traditionally, PID controllers have been...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/173586 |
| _version_ | 1811692191389581312 |
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| author | Dharmalingam, Yagneshwar |
| author2 | Arokiaswami Alphones |
| author_facet | Arokiaswami Alphones Dharmalingam, Yagneshwar |
| author_sort | Dharmalingam, Yagneshwar |
| collection | NTU |
| description | The Building Management System (BMS) can monitor and manage a facility's mechanical,
electrical, and electromechanical services. One subset of this system is Air-conditioning and
Mechanical Ventilation (ACMV), which regulates interior temperatures. Traditionally, PID
controllers have been used in ACMV subsystems to achieve desired temperature control outcomes;
however, such controllers are associated with real-time constraints that limit their effectiveness. In
an effort to address these limitations inherent in PID-based systems, Model Predictive Control
(MPC) has emerged as a promising alternative controller capable of surmounting various issues
pertaining to tracking, accuracy, etc. Additionally, thanks to recent improvements in machine
learning algorithms, it can now handle large quantities of data efficiently while employing edge
devices for operational simplicity. This study thus examines the integration of MPC architecture
alongside machine learning algorithms into edge-based HVAC solutions within testbed scenarios.
The results will be compared against the existing baseline BMS data on various factors such as
power consumption. |
| first_indexed | 2024-10-01T06:31:52Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/173586 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:31:52Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1735862024-02-16T15:42:57Z Machine learning-based model predictive control for ACMV system using edge device Dharmalingam, Yagneshwar Arokiaswami Alphones School of Electrical and Electronic Engineering EAlphones@ntu.edu.sg Engineering The Building Management System (BMS) can monitor and manage a facility's mechanical, electrical, and electromechanical services. One subset of this system is Air-conditioning and Mechanical Ventilation (ACMV), which regulates interior temperatures. Traditionally, PID controllers have been used in ACMV subsystems to achieve desired temperature control outcomes; however, such controllers are associated with real-time constraints that limit their effectiveness. In an effort to address these limitations inherent in PID-based systems, Model Predictive Control (MPC) has emerged as a promising alternative controller capable of surmounting various issues pertaining to tracking, accuracy, etc. Additionally, thanks to recent improvements in machine learning algorithms, it can now handle large quantities of data efficiently while employing edge devices for operational simplicity. This study thus examines the integration of MPC architecture alongside machine learning algorithms into edge-based HVAC solutions within testbed scenarios. The results will be compared against the existing baseline BMS data on various factors such as power consumption. Master's degree 2024-02-16T01:27:42Z 2024-02-16T01:27:42Z 2023 Thesis-Master by Coursework Dharmalingam, Y. (2023). Machine learning-based model predictive control for ACMV system using edge device. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/173586 https://hdl.handle.net/10356/173586 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering Dharmalingam, Yagneshwar Machine learning-based model predictive control for ACMV system using edge device |
| title | Machine learning-based model predictive control for ACMV system using edge device |
| title_full | Machine learning-based model predictive control for ACMV system using edge device |
| title_fullStr | Machine learning-based model predictive control for ACMV system using edge device |
| title_full_unstemmed | Machine learning-based model predictive control for ACMV system using edge device |
| title_short | Machine learning-based model predictive control for ACMV system using edge device |
| title_sort | machine learning based model predictive control for acmv system using edge device |
| topic | Engineering |
| url | https://hdl.handle.net/10356/173586 |
| work_keys_str_mv | AT dharmalingamyagneshwar machinelearningbasedmodelpredictivecontrolforacmvsystemusingedgedevice |