Occupancy modelling using data driven models

Heating, Ventilation and Air-conditioning (HVAC) systems are typically designed using static extreme values, resulting in them being over dimensioned for most of their operating time. To reduce the energy wastage, efficient control of the usage of HVAC can be done through occupancy sensing. Effectiv...

Full description

Saved in:
Bibliographic Details
Main Author: Teng, Sherlyn Xue Qi
Other Authors: Soh Yeng Chai
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/167451
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-167451
record_format dspace
spelling sg-ntu-dr.10356-1674512023-07-07T17:08:31Z Occupancy modelling using data driven models Teng, Sherlyn Xue Qi Soh Yeng Chai School of Electrical and Electronic Engineering EYCSOH@ntu.edu.sg Engineering::Electrical and electronic engineering Heating, Ventilation and Air-conditioning (HVAC) systems are typically designed using static extreme values, resulting in them being over dimensioned for most of their operating time. To reduce the energy wastage, efficient control of the usage of HVAC can be done through occupancy sensing. Effective feature engineering methods will be used to estimate the accuracy of the occupancy in the building. Non-intrusive aspect of the environmental sensors such as the CO2, humidity, lighting, and temperature is used to collect data. The approach of visualising the different features and analyse its usefulness in estimating the occupancy in the enclosed space will be analysed. In this discussion, various machine learning methods are used to model the occupancy and estimate the accuracy. The models with the best accuracies will be presented and further discussed. Bachelor of Engineering (Information Engineering and Media) 2023-05-26T06:36:16Z 2023-05-26T06:36:16Z 2023 Final Year Project (FYP) Teng, S. X. Q. (2023). Occupancy modelling using data driven models. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/167451 https://hdl.handle.net/10356/167451 en 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
Teng, Sherlyn Xue Qi
Occupancy modelling using data driven models
description Heating, Ventilation and Air-conditioning (HVAC) systems are typically designed using static extreme values, resulting in them being over dimensioned for most of their operating time. To reduce the energy wastage, efficient control of the usage of HVAC can be done through occupancy sensing. Effective feature engineering methods will be used to estimate the accuracy of the occupancy in the building. Non-intrusive aspect of the environmental sensors such as the CO2, humidity, lighting, and temperature is used to collect data. The approach of visualising the different features and analyse its usefulness in estimating the occupancy in the enclosed space will be analysed. In this discussion, various machine learning methods are used to model the occupancy and estimate the accuracy. The models with the best accuracies will be presented and further discussed.
author2 Soh Yeng Chai
author_facet Soh Yeng Chai
Teng, Sherlyn Xue Qi
format Final Year Project
author Teng, Sherlyn Xue Qi
author_sort Teng, Sherlyn Xue Qi
title Occupancy modelling using data driven models
title_short Occupancy modelling using data driven models
title_full Occupancy modelling using data driven models
title_fullStr Occupancy modelling using data driven models
title_full_unstemmed Occupancy modelling using data driven models
title_sort occupancy modelling using data driven models
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/167451
_version_ 1772828219123171328