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Optimization of HVAC system energy consumption in a building using artificial neural network and multi-objective genetic algorithm

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dc.citedby123
dc.contributor.authorNasruddinen_US
dc.contributor.authorSholahudinen_US
dc.contributor.authorSatrio P.en_US
dc.contributor.authorMahlia T.M.I.en_US
dc.contributor.authorGiannetti N.en_US
dc.contributor.authorSaito K.en_US
dc.contributor.authorid57211141063en_US
dc.contributor.authorid57202068616en_US
dc.contributor.authorid57193959584en_US
dc.contributor.authorid56997615100en_US
dc.contributor.authorid56671465200en_US
dc.contributor.authorid55105152500en_US
dc.date.accessioned2023-05-29T07:23:30Z
dc.date.available2023-05-29T07:23:30Z
dc.date.issued2019
dc.descriptionAir conditioning; Buildings; Cooling systems; Decision making; Energy conservation; Energy utilization; Genetic algorithms; Multiobjective optimization; Neural networks; Thermal comfort; Annual energy consumption; Building energy consumption; Building parameters; Dedicated outdoor air systems; Multi-objective genetic algorithm; Objective functions; Passive solar design; Radiant cooling; HVAC; air conditioning; artificial neural network; building; cooling; energy use; genetic algorithm; optimization; temperature effecten_US
dc.description.abstractThe optimization of heating, ventilating and air conditioning (HVAC) system operations and other building parameters intended to minimize annual energy consumption and maximize the thermal comfort is presented in this paper. The combination of artificial neural network (ANN) and multi-objective genetic algorithm (MOGA) is applied to optimize the two-chiller system operation in a building. The HVAC system installed in the building integrates radiant cooling system, variable air volume (VAV) chiller system, and dedicated outdoor air system (DOAS). Several parameters including thermostat setting, passive solar design, and chiller operation control are considered as decision variables. Subsequently, the percentage of people dissatisfied (PPD) and annual building energy consumption is chosen as objective functions. Multi-objective optimization is employed to optimize the system with two objective functions. As the result, ANN performed a good correlation between decision variables and the objective function. Moreover, MOGA successfully provides several alternative possible design variables to achieve optimum system in terms of thermal comfort and annual energy consumption. In conclusion, the optimization that considers two objectives shows the best result regarding thermal comfort and energy consumption compared to base case design. � 2019 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.seta.2019.06.002
dc.identifier.epage57
dc.identifier.scopus2-s2.0-85067884492
dc.identifier.spage48
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85067884492&doi=10.1016%2fj.seta.2019.06.002&partnerID=40&md5=e42ee199ff7314d3a8ddadc1a6564a78
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24438
dc.identifier.volume35
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleSustainable Energy Technologies and Assessments
dc.titleOptimization of HVAC system energy consumption in a building using artificial neural network and multi-objective genetic algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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