Data analytics and modelling for indoor occupied states

This research focused on data analytics and modelling for indoor occupied states in smart buildings, with the aim of improving energy efficiency and environmental sustainability by accurately predicting and controlling indoor occupancy states. The study was based on five months of real smart buildin...

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Main Author: Li, Yongjie
Other Authors: Soh Yeng Chai
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/177070
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1770702024-05-24T15:44:45Z Data analytics and modelling for indoor occupied states Li, Yongjie Soh Yeng Chai School of Electrical and Electronic Engineering EYCSOH@ntu.edu.sg Engineering Data analytics This research focused on data analytics and modelling for indoor occupied states in smart buildings, with the aim of improving energy efficiency and environmental sustainability by accurately predicting and controlling indoor occupancy states. The study was based on five months of real smart building data provided by Keppel Ltd., mainly sensor data, including carbon dioxide concentration, humidity and temperature. In the initial stage of the project, unsupervised learning methods were used to perform cluster analysis and feature importance assessment, and successfully identified the influence of indoor occupancy patterns and environmental indicators on the prediction model. Subsequently, supervised learning models were used to predict the indoor occupancy states, with an accuracy of up to 99%. Leveraging these predictive models and analyzing historical unoccupied periods during working hours, suggested that energy savings of about 12% could theoretically be achieved through intelligent regulation. This significant finding not only provided a strong support for the energy efficiency management of smart buildings, but also indicated that energy use efficiency, resource utilization efficiency and indoor environmental quality could be significantly improved through fine prediction and control of indoor occupancy states. Bachelor's degree 2024-05-23T11:51:46Z 2024-05-23T11:51:46Z 2024 Final Year Project (FYP) Li, Y. (2024). Data analytics and modelling for indoor occupied states. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/177070 https://hdl.handle.net/10356/177070 en B1092-231 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
Data analytics
spellingShingle Engineering
Data analytics
Li, Yongjie
Data analytics and modelling for indoor occupied states
description This research focused on data analytics and modelling for indoor occupied states in smart buildings, with the aim of improving energy efficiency and environmental sustainability by accurately predicting and controlling indoor occupancy states. The study was based on five months of real smart building data provided by Keppel Ltd., mainly sensor data, including carbon dioxide concentration, humidity and temperature. In the initial stage of the project, unsupervised learning methods were used to perform cluster analysis and feature importance assessment, and successfully identified the influence of indoor occupancy patterns and environmental indicators on the prediction model. Subsequently, supervised learning models were used to predict the indoor occupancy states, with an accuracy of up to 99%. Leveraging these predictive models and analyzing historical unoccupied periods during working hours, suggested that energy savings of about 12% could theoretically be achieved through intelligent regulation. This significant finding not only provided a strong support for the energy efficiency management of smart buildings, but also indicated that energy use efficiency, resource utilization efficiency and indoor environmental quality could be significantly improved through fine prediction and control of indoor occupancy states.
author2 Soh Yeng Chai
author_facet Soh Yeng Chai
Li, Yongjie
format Final Year Project
author Li, Yongjie
author_sort Li, Yongjie
title Data analytics and modelling for indoor occupied states
title_short Data analytics and modelling for indoor occupied states
title_full Data analytics and modelling for indoor occupied states
title_fullStr Data analytics and modelling for indoor occupied states
title_full_unstemmed Data analytics and modelling for indoor occupied states
title_sort data analytics and modelling for indoor occupied states
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/177070
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