Information fusion for HVAC application

The developments of pervasive sensors for Heating, Ventilation and Air Conditioning (HVAC) process has been attracting increasing attention over the years. Information from these sensors carries complex interrelationships between the thermodynamic parameter of HVAC. Therefore, the extractio...

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Main Author: Leong, Wei Jie
Other Authors: Soh Yeng Chai
Format: Final Year Project
Language:English
Published: 2014
Subjects:
Online Access:http://hdl.handle.net/10356/61407
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-614072023-07-07T16:35:32Z Information fusion for HVAC application Leong, Wei Jie Soh Yeng Chai School of Electrical and Electronic Engineering DRNTU::Engineering The developments of pervasive sensors for Heating, Ventilation and Air Conditioning (HVAC) process has been attracting increasing attention over the years. Information from these sensors carries complex interrelationships between the thermodynamic parameter of HVAC. Therefore, the extraction of key relationships from these pervasive sensors that drive the heat exchange phenomena, including their interactions with the environment and users are to be discovered. Information fusion from multi-modal data source, ASHRAE RP-884 Adaptive Model of Thermal Comfort and Performance with simulations using MATLAB® is proposed to analyse the heat exchange phenomena. Feed forward neural networks model, Extreme Learning Machines (ELM) is used to analysis the testing accuracy based on the target values and its variables. Correlations of variables with PMV and ASHRAE shows different outcome with additional variables through ELM testing accuracy. This study shows how correlations were established using ELM with given data variables and randomly generated variables. Six primary variables of predicted mean vote (PMV) index calculation by Fanger were used from RP-884 data and randomly generated variables. Addition of available data variables from RP-884 is used to discover the relationship of PMV and ASHRAE thermal sensation scale. The target values used for the ELM technique are PMV and ASHARAE thermal sensational scale. ELM results shown that the testing accuracy does not necessary improve with additional variables Bachelor of Engineering 2014-06-10T02:52:25Z 2014-06-10T02:52:25Z 2014 2014 Final Year Project (FYP) http://hdl.handle.net/10356/61407 en Nanyang Technological University 60 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering
spellingShingle DRNTU::Engineering
Leong, Wei Jie
Information fusion for HVAC application
description The developments of pervasive sensors for Heating, Ventilation and Air Conditioning (HVAC) process has been attracting increasing attention over the years. Information from these sensors carries complex interrelationships between the thermodynamic parameter of HVAC. Therefore, the extraction of key relationships from these pervasive sensors that drive the heat exchange phenomena, including their interactions with the environment and users are to be discovered. Information fusion from multi-modal data source, ASHRAE RP-884 Adaptive Model of Thermal Comfort and Performance with simulations using MATLAB® is proposed to analyse the heat exchange phenomena. Feed forward neural networks model, Extreme Learning Machines (ELM) is used to analysis the testing accuracy based on the target values and its variables. Correlations of variables with PMV and ASHRAE shows different outcome with additional variables through ELM testing accuracy. This study shows how correlations were established using ELM with given data variables and randomly generated variables. Six primary variables of predicted mean vote (PMV) index calculation by Fanger were used from RP-884 data and randomly generated variables. Addition of available data variables from RP-884 is used to discover the relationship of PMV and ASHRAE thermal sensation scale. The target values used for the ELM technique are PMV and ASHARAE thermal sensational scale. ELM results shown that the testing accuracy does not necessary improve with additional variables
author2 Soh Yeng Chai
author_facet Soh Yeng Chai
Leong, Wei Jie
format Final Year Project
author Leong, Wei Jie
author_sort Leong, Wei Jie
title Information fusion for HVAC application
title_short Information fusion for HVAC application
title_full Information fusion for HVAC application
title_fullStr Information fusion for HVAC application
title_full_unstemmed Information fusion for HVAC application
title_sort information fusion for hvac application
publishDate 2014
url http://hdl.handle.net/10356/61407
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