Machine learning based energy evaluation using air temperature and air velocity

The talk of the generation has been on one particular topic, climate change. Though it has been ongoing for years or even decades, it still is a topic of concern amongst many. Development in technology and quality of life has put humans on the forefront but the very environment we are living in may...

Full description

Saved in:
Bibliographic Details
Main Author: Mohamed Farhan Mohamed Farouk
Other Authors: Soh Yeng Chai
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2020
Subjects:
Online Access:https://hdl.handle.net/10356/138742
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-138742
record_format dspace
spelling sg-ntu-dr.10356-1387422023-07-07T18:20:25Z Machine learning based energy evaluation using air temperature and air velocity Mohamed Farhan Mohamed Farouk Soh Yeng Chai School of Electrical and Electronic Engineering eycsoh@ntu.edu.sg Engineering::Computer science and engineering::Software::Programming languages Engineering::Electrical and electronic engineering The talk of the generation has been on one particular topic, climate change. Though it has been ongoing for years or even decades, it still is a topic of concern amongst many. Development in technology and quality of life has put humans on the forefront but the very environment we are living in may be subjected to damages which may go unrealised. A large part of climate and environmental changes has to do with inefficient usage and wastage of energy and electricity. Climate change affects the entire planet and Singapore is no exception. Despite several efforts to reduce carbon emissions, as a nation there are plenty more to be done to deliver greater positive impacts. In Singapore, buildings, both residential and non-residential consume plenty of energy through the use of Heating Ventilation and Air Conditioning (HVAC) and Air Conditioning and Mechanical Ventilation (ACMV) systems. Apart from focusing on energy efficiency and cost savings, this project aims to increase energy efficiency of these systems whilst maintaining a satisfactory level of thermal comfort for occupants in buildings. Machine Learning is employed in MATLAB to analyse the association of energy consumption with thermal comfort levels. Neural Networks models and Extreme Learning Machine are experimented with the data obtained to evaluate thermal comfort and the Predicted Mean Vote (PMV) Index. Ultimately, the optimisation algorithm would be able to provide a solution to attain energy efficiency in HVAC systems without compromising on the thermal comfort levels of the occupants in rooms and buildings. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-05-12T05:54:52Z 2020-05-12T05:54:52Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/138742 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::Computer science and engineering::Software::Programming languages
Engineering::Electrical and electronic engineering
spellingShingle Engineering::Computer science and engineering::Software::Programming languages
Engineering::Electrical and electronic engineering
Mohamed Farhan Mohamed Farouk
Machine learning based energy evaluation using air temperature and air velocity
description The talk of the generation has been on one particular topic, climate change. Though it has been ongoing for years or even decades, it still is a topic of concern amongst many. Development in technology and quality of life has put humans on the forefront but the very environment we are living in may be subjected to damages which may go unrealised. A large part of climate and environmental changes has to do with inefficient usage and wastage of energy and electricity. Climate change affects the entire planet and Singapore is no exception. Despite several efforts to reduce carbon emissions, as a nation there are plenty more to be done to deliver greater positive impacts. In Singapore, buildings, both residential and non-residential consume plenty of energy through the use of Heating Ventilation and Air Conditioning (HVAC) and Air Conditioning and Mechanical Ventilation (ACMV) systems. Apart from focusing on energy efficiency and cost savings, this project aims to increase energy efficiency of these systems whilst maintaining a satisfactory level of thermal comfort for occupants in buildings. Machine Learning is employed in MATLAB to analyse the association of energy consumption with thermal comfort levels. Neural Networks models and Extreme Learning Machine are experimented with the data obtained to evaluate thermal comfort and the Predicted Mean Vote (PMV) Index. Ultimately, the optimisation algorithm would be able to provide a solution to attain energy efficiency in HVAC systems without compromising on the thermal comfort levels of the occupants in rooms and buildings.
author2 Soh Yeng Chai
author_facet Soh Yeng Chai
Mohamed Farhan Mohamed Farouk
format Final Year Project
author Mohamed Farhan Mohamed Farouk
author_sort Mohamed Farhan Mohamed Farouk
title Machine learning based energy evaluation using air temperature and air velocity
title_short Machine learning based energy evaluation using air temperature and air velocity
title_full Machine learning based energy evaluation using air temperature and air velocity
title_fullStr Machine learning based energy evaluation using air temperature and air velocity
title_full_unstemmed Machine learning based energy evaluation using air temperature and air velocity
title_sort machine learning based energy evaluation using air temperature and air velocity
publisher Nanyang Technological University
publishDate 2020
url https://hdl.handle.net/10356/138742
_version_ 1772826353310105600