Energy modeling and control for a thermostatically controlled load (TCL) using intelligent optimization algorithms

dc.contributor.advisorDiduch, Chris
dc.contributor.advisorKaye, Mary
dc.contributor.authorYan, Keming
dc.date.accessioned2023-03-01T16:16:21Z
dc.date.available2023-03-01T16:16:21Z
dc.date.issued2019
dc.date.updated2023-03-01T15:01:05Z
dc.description.abstractIn this research work, we proposed an on-and-off parameter identification methodology for establishing a single-zone TCL model. A case study was conducted to validate the proposed model. A temperature prediction technique was also developed for a single-zone Heating, Ventilation and Air Conditioning (HVAC) system. We also conducted another case study in the Wu Conference Centre at the University of New Brunswick (UNB) to validate this technique. A Multi-Objective Genetic Algorithm (MOGA)-based intelligent control strategy for load shifting and energy conservation was then developed. It integrated the occupancy information, Time-of-Use (ToU) pricing, and the temperature prediction technique into the optimization process, and used the single-zone TCL model as a testbed to evaluate the control performance. All simulations of this research work were built and tested in MATLAB® R2017a.
dc.description.copyright© Keming Yan, 2019
dc.formattext/xml
dc.format.extentxxx, 209 pages
dc.format.mediumelectronic
dc.identifier.urihttps://unbscholar.lib.unb.ca/handle/1882/13205
dc.language.isoen_CA
dc.publisherUniversity of New Brunswick
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subject.disciplineElectrical and Computer Engineering
dc.titleEnergy modeling and control for a thermostatically controlled load (TCL) using intelligent optimization algorithms
dc.typemaster thesis
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.fullnameMaster of Science in Engineering
thesis.degree.grantorUniversity of New Brunswick
thesis.degree.levelmasters
thesis.degree.nameM.Sc.E.

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