Optimization of Night Cooling in Commercial Buildings - Using Genetic Algorithms and Neural Networks

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dc.contributor.authorDahlström, Emmy
dc.contributor.authorRönn, Linus
dc.contributor.departmentChalmers tekniska högskola / Institutionen för bygg- och miljötekniksv
dc.contributor.departmentChalmers University of Technology / Department of Civil and Environmental Engineeringen
dc.date.accessioned2019-07-03T14:39:13Z
dc.date.available2019-07-03T14:39:13Z
dc.date.issued2017
dc.description.abstractPeriodically since the two oil crisis in the 1970s, there has been a focus in Sweden on reducing the energy use in buildings. This focus has evolved into the building regulations used today. With the stricter energy requirements and the interest from society in environmental issues there is a need to use more optimized control systems for cooling and ventilation. Night cooling is an example of this. The purpose of night cooling is to decrease the cooling need in buildings by ventilating at night with cold outdoor air. The thesis uses case studies to examine if it is possible to optimize night cooling set points and time schedules regarding energy consumption and indoor climate for two retail stores in Göteborg. The optimizations were done with genetic algorithms, neural networks and building energy models, based on logged control data for the two stores. The study suggest that the energy consumption could be reduced with 15% for both facilities with the optimized control settings compared to the original. The project also shows that even unoptimized night cooling has benefits to energy consumption. The sensitivity analysis shows that a reduction around 10% for similar buildings are plausible with the optimized settings from the case studies. The projects concludes that the use of logged control data in combination with genetic algorithms and neural networks is an efficient way for both calibration and optimization of building energy models. The industry moves towards an increase of available logged control data. As such, it is important to be able to properly utilize the data for improving the accuracy of building energy simulations. The method used in this project is an example of this.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/253023
dc.language.isoeng
dc.relation.ispartofseriesExamensarbete - Institutionen för bygg- och miljöteknik, Chalmers tekniska högskola : BOMX02-17-50
dc.setspec.uppsokTechnology
dc.subjectMaterialvetenskap
dc.subjectByggnadsteknik
dc.subjectMaterials Science
dc.subjectBuilding engineering
dc.titleOptimization of Night Cooling in Commercial Buildings - Using Genetic Algorithms and Neural Networks
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeStructural engineering and building technology (MPSEB), MSc
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