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...

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Main Author: Ong, Zi Heng
Other Authors: Soh Yeng Chai
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
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Online Access:https://hdl.handle.net/10356/167343
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Institution: Nanyang Technological University
Language: English
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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
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