Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/60821
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dc.contributor.authorTeo, Sharon Hui Ling
dc.date.accessioned2014-05-30T08:33:27Z
dc.date.available2014-05-30T08:33:27Z
dc.date.copyright2014en_US
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/10356/60821
dc.description.abstractThis project is to explore the use of machine learning technique such as ANN, ELM etc to derive at a better human comfort analysis and evaluation of air-conditioned spaces. Very often, the human comfort are derived based on certain empirical formulae derived on certain condiitons, and may not be appropriate for tropical setttings like in Singapore. In modern HVAC systems, much more information are available about the operation conditions of the systems. These information can best be exploited by using machine learning techniques to extract the imporatnt influencing factors on human comfort. Discoveries made using the machine learning techniques can be captured and analyzed to identify the important parameters that determine the human comfort of air-condiitoned spaces in a tropical setting. With these information, the impacts from changes in the layout of the building, the operating conditions, the air flow, the temperature, the humidity etc can be readily and quickly examined with respect to human comfort.en_US
dc.format.extent62 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineeringen_US
dc.titleMachine learning techniques for human comfort evaluation of HVAC systemsen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorSoh Yeng Chaien_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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