Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/90112
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dc.contributor.authorZhai, Deqingen
dc.date.accessioned2019-05-29T03:54:42Zen
dc.date.accessioned2019-12-06T17:40:54Z-
dc.date.available2019-05-29T03:54:42Zen
dc.date.available2019-12-06T17:40:54Z-
dc.date.issued2019en
dc.identifier.citationZhai, D. (2019). Modeling and optimization of ACMV systems for energy efficient smart buildings. Doctoral thesis, Nanyang Technological University, Singapore.en
dc.identifier.urihttps://hdl.handle.net/10356/90112-
dc.description.abstractModeling and optimization for energy efficient smart buildings are interesting and promising research areas. According to Paris Protocol signed in 2015, energy efficient, smart and green buildings are imperative concerns. Heating, ventilation and air-conditioning (HVAC) or air conditioning and mechanical ventilation (ACMV) systems, consume around 40% of the total energy, and the systems also directly impact on the environmental conditions, especially the indoor environmental conditions, such as air temperature, air humidity, air velocity, air quality, etc. In this thesis, the main objective is to systematically optimize the ACMV systems to operate efficiently and maintain indoor environmental conditions as comfortable and healthy as possible for occupants. The thesis is organized into the following systematic three-phase methodology to enhance ACMV systems' energy efficiency and indoor occupants' thermal comfort in smart buildings. Phase 1: Modeling energy consumption of ACMV systems with machine learning techniques. Phase 2: Modeling thermal comfort sensations of occupants with passive and active approaches. Phase 3: Formulating and solving optimization problems to enhance smart buildings' energy efficiency and maintaining indoor thermal comfort sensations of occupants under different algorithms.en
dc.format.extent229 p.en
dc.language.isoenen
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen
dc.titleModeling and optimization of ACMV systems for energy efficient smart buildingsen
dc.typeThesisen
dc.contributor.supervisorSoh Yeng Chaien
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.description.degreeDoctor of Philosophyen
dc.identifier.doi10.32657/10220/48443en
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Appears in Collections:EEE Theses
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