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Leslie K. Norford. Jain, Ruchi V Massachusetts Institute of Technology. Dept. of Mechanical Engineering. Massachusetts Institute of Technology. Department of Mechanical Engineering 2008-02-27T22:26:33Z 2008-02-27T22:26:33Z 2007 2007 http://hdl.handle.net/1721.1/40445 191730747 Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2007. Includes bibliographical references (p. 34). When designing buildings, energy analysis is typically done after construction has been completed, but making the design decisions while keeping energy efficiency in mind, is one way to make energy-efficient buildings. The conscious design of building parameters could decrease or completely eliminate the need for Heating, Ventilation and Air Conditioning systems, and thus, optimizing building parameters could help conserve a great amount of energy. This work focuses on two buildings - a passive solar house and an apartment in Beijing. The Beijing apartment is used to study natural ventilation in a space. Both buildings are modeled using EnergyPlus, and analyzed using VBA in Excel. The Genetic Algorithm Optimization Toolbox (GAOT) is used to optimize the parameters for the solar house. The program was run for 150 generations, with there being 20 individuals in each population. The optimized parameters for the solar house resulted in a mean internal temperature of 20.1 C, 7 C lower than that for randomly chosen parameters. The extreme temperatures in both cases were also markedly different, with the optimized parameters providing a more comfortable atmosphere in the house. (cont.) The apartment parameters were not optimized due to the inherent difficulty in quantifying an objective function. Through the simulation however, it was determined that each window has mass inflow and outflow occurring at the same time. In order to check that mass was conserved through the flow of air in and out of the apartment, the net flow in or out through each window had to be considered. This comparison did show the conservation of mass, which provided confidence in the EnergyPlus model used. by Ruchi V. Jain. S.B. 84 p. eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Mechanical Engineering. Optimization of energy parameters in buildings Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="49f52e52-13e9-4e42-93da-9e3013314cf7"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Optimization of energy parameters in buildings</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2007</PublicationDate> <Authors> <Author> <DisplayName>Jain, Ruchi V</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Mechanical Engineering.</Keyword> <Abstract>When designing buildings, energy analysis is typically done after construction has been completed, but making the design decisions while keeping energy efficiency in mind, is one way to make energy-efficient buildings. The conscious design of building parameters could decrease or completely eliminate the need for Heating, Ventilation and Air Conditioning systems, and thus, optimizing building parameters could help conserve a great amount of energy. This work focuses on two buildings - a passive solar house and an apartment in Beijing. The Beijing apartment is used to study natural ventilation in a space. Both buildings are modeled using EnergyPlus, and analyzed using VBA in Excel. The Genetic Algorithm Optimization Toolbox (GAOT) is used to optimize the parameters for the solar house. The program was run for 150 generations, with there being 20 individuals in each population. The optimized parameters for the solar house resulted in a mean internal temperature of 20.1 C, 7 C lower than that for randomly chosen parameters. The extreme temperatures in both cases were also markedly different, with the optimized parameters providing a more comfortable atmosphere in the house.</Abstract> <Abstract>(cont.) The apartment parameters were not optimized due to the inherent difficulty in quantifying an objective function. Through the simulation however, it was determined that each window has mass inflow and outflow occurring at the same time. In order to check that mass was conserved through the flow of air in and out of the apartment, the net flow in or out through each window had to be considered. This comparison did show the conservation of mass, which provided confidence in the EnergyPlus model used.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>