Data analytics, modelling, and optimization on HVAC systems
A Heating, Ventilation, and Air Conditioning (HVAC) system provides heating and cooling services to buildings and other enclosed spaces. They are designed to control temperature, air quality, and humidity to create a comfortable indoor environment based on set points. In this industry-supporte...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/167343 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-167343 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1673432023-07-07T15:45:45Z Data analytics, modelling, and optimization on HVAC systems Ong, Zi Heng Soh Yeng Chai School of Electrical and Electronic Engineering Keppel Land EYCSOH@ntu.edu.sg Engineering::Electrical and electronic engineering A Heating, Ventilation, and Air Conditioning (HVAC) system provides heating and cooling services to buildings and other enclosed spaces. They are designed to control temperature, air quality, and humidity to create a comfortable indoor environment based on set points. In this industry-supported project, the HVAC system of the building, Keppel Bay Tower (KBT), which has 18 floors, is to be studied for optimization. The project first aims to use data gathered from the building’s Internet of Things sensors to perform data analytics to garner insights on data that may prove useful in identifying the optimality conditions of the system. After data pre processing, the Air Handling Unit (AHU), Chiller (CH), Chiller Water Pump (CHWP), Condenser Water Pump (CWP), and Cooling Tower Fans (CTF) were identified to be the relevant components with useful data. The presence of missing, incomplete, and spurious data was also identified. Relevant measure points were then identified, and through correlation and data analysis, it was found that in general, the CH and CWP component types contributed most efficiently to cooling. Both general and specific trends for each AHU were also uncovered for monitoring, with emphasis placed on components that play a more significant role in cooling. Data-driven models were then developed along with a feature selection study for energy consumption prediction and used to optimize the operations of the HVAC system. The models developed include a Random Forest for Model 0 and Neural Network models for Models 1 and 2. The models were run with a 20% reduction in total power consumption simulated. Poor model evaluation results were produced by Model 0 along with inaccurate predictions of the output temperature, as this model only took in the input temperature and the highest correlated feature of total power. Feature selection was then carried out, and Models 1 and 2 were set to simulate the Chilled Water (CHW) cycle and AHU activities respectfully, with many more inputs. Hence, Model 1 was able to produce highly accurate predictions of the temperature of the CHW supply, and Model 2 was also able to produce accurate predictions of output temperature given Model 1’s input. This process identified that a 20% reduction in power consumption is a viable option to optimize the HVAC system while ensuring comfortable temperatures are kept on weekdays and possibly weekends. Bachelor of Engineering (Electrical and Electronic Engineering) 2023-05-25T05:53:52Z 2023-05-25T05:53:52Z 2023 Final Year Project (FYP) Ong, Z. H. (2023). Data analytics, modelling, and optimization on HVAC systems. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/167343 https://hdl.handle.net/10356/167343 en B1022-221 application/pdf Nanyang Technological University |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Engineering::Electrical and electronic engineering |
spellingShingle |
Engineering::Electrical and electronic engineering Ong, Zi Heng Data analytics, modelling, and optimization on HVAC systems |
description |
A Heating, Ventilation, and Air Conditioning (HVAC) system provides heating and
cooling services to buildings and other enclosed spaces. They are designed to control
temperature, air quality, and humidity to create a comfortable indoor environment
based on set points. In this industry-supported project, the HVAC system of the
building, Keppel Bay Tower (KBT), which has 18 floors, is to be studied for
optimization. The project first aims to use data gathered from the building’s Internet
of Things sensors to perform data analytics to garner insights on data that may prove
useful in identifying the optimality conditions of the system. After data pre processing, the Air Handling Unit (AHU), Chiller (CH), Chiller Water Pump
(CHWP), Condenser Water Pump (CWP), and Cooling Tower Fans (CTF) were
identified to be the relevant components with useful data. The presence of missing,
incomplete, and spurious data was also identified. Relevant measure points were then
identified, and through correlation and data analysis, it was found that in general, the
CH and CWP component types contributed most efficiently to cooling. Both general
and specific trends for each AHU were also uncovered for monitoring, with emphasis
placed on components that play a more significant role in cooling.
Data-driven models were then developed along with a feature selection study for
energy consumption prediction and used to optimize the operations of the HVAC
system. The models developed include a Random Forest for Model 0 and Neural
Network models for Models 1 and 2. The models were run with a 20% reduction in
total power consumption simulated. Poor model evaluation results were produced by
Model 0 along with inaccurate predictions of the output temperature, as this model
only took in the input temperature and the highest correlated feature of total power.
Feature selection was then carried out, and Models 1 and 2 were set to simulate the
Chilled Water (CHW) cycle and AHU activities respectfully, with many more inputs.
Hence, Model 1 was able to produce highly accurate predictions of the temperature of
the CHW supply, and Model 2 was also able to produce accurate predictions of output
temperature given Model 1’s input. This process identified that a 20% reduction in
power consumption is a viable option to optimize the HVAC system while ensuring
comfortable temperatures are kept on weekdays and possibly weekends. |
author2 |
Soh Yeng Chai |
author_facet |
Soh Yeng Chai Ong, Zi Heng |
format |
Final Year Project |
author |
Ong, Zi Heng |
author_sort |
Ong, Zi Heng |
title |
Data analytics, modelling, and optimization on HVAC systems |
title_short |
Data analytics, modelling, and optimization on HVAC systems |
title_full |
Data analytics, modelling, and optimization on HVAC systems |
title_fullStr |
Data analytics, modelling, and optimization on HVAC systems |
title_full_unstemmed |
Data analytics, modelling, and optimization on HVAC systems |
title_sort |
data analytics, modelling, and optimization on hvac systems |
publisher |
Nanyang Technological University |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/167343 |
_version_ |
1772825167897034752 |