Comfort and living environment analysis in smart living environment
This project will be focus on achieving the integrative sensor-enabled information system for indoor environment. PMV model is used as a standard model for analyzing the relationship between six input parameters; Air Temperature, Air Velocity, Humidity, Mean Radiant Temperature, Clothing Level and M...
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2018
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sg-ntu-dr.10356-750322023-07-07T16:47:20Z Comfort and living environment analysis in smart living environment Khine, Wint Wah Soh Yeng Chai School of Electrical and Electronic Engineering Soh Yeng Chai DRNTU::Engineering This project will be focus on achieving the integrative sensor-enabled information system for indoor environment. PMV model is used as a standard model for analyzing the relationship between six input parameters; Air Temperature, Air Velocity, Humidity, Mean Radiant Temperature, Clothing Level and Metabolic Rate. By applying this understanding of each input parameter correlation, thermal comfort model is implemented by using machine learning techniques and Neural Net Fitting Tool in MATLAB software application. Using most persistent and reliable thermal comfort model for integrative sensor-enabled information system, real time quality of indoor environment can be evaluated for better smart living indoor environment. Bachelor of Engineering 2018-05-27T12:58:33Z 2018-05-27T12:58:33Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/75032 en Nanyang Technological University 54 p. application/pdf |
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DRNTU::Engineering Khine, Wint Wah Comfort and living environment analysis in smart living environment |
| description |
This project will be focus on achieving the integrative sensor-enabled information system for indoor environment. PMV model is used as a standard model for analyzing the relationship between six input parameters; Air Temperature, Air Velocity, Humidity, Mean Radiant Temperature, Clothing Level and Metabolic Rate.
By applying this understanding of each input parameter correlation, thermal comfort model is implemented by using machine learning techniques and Neural Net Fitting Tool in MATLAB software application.
Using most persistent and reliable thermal comfort model for integrative sensor-enabled information system, real time quality of indoor environment can be evaluated for better smart living indoor environment. |
| author2 |
Soh Yeng Chai |
| author_facet |
Soh Yeng Chai Khine, Wint Wah |
| format |
Final Year Project |
| author |
Khine, Wint Wah |
| author_sort |
Khine, Wint Wah |
| title |
Comfort and living environment analysis in smart living environment |
| title_short |
Comfort and living environment analysis in smart living environment |
| title_full |
Comfort and living environment analysis in smart living environment |
| title_fullStr |
Comfort and living environment analysis in smart living environment |
| title_full_unstemmed |
Comfort and living environment analysis in smart living environment |
| title_sort |
comfort and living environment analysis in smart living environment |
| publishDate |
2018 |
| url |
http://hdl.handle.net/10356/75032 |
| _version_ |
1772828167660109824 |