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Please use this identifier to cite or link to this item: http://dspace.vgtu.lt/handle/1/3743

Title: Artificial neural network-based decision support system for development of an energy-efficient built environment
Authors: Kaklauskas, Artūras
Dzemyda, Gintautas
Tupėnaitė, Laura
Voitau, Ihar
Kurasova, Olga
Naimavičienė, Jurga
Rassokha, Yauheni
Kanapeckienė, Loreta
Keywords: energy-efficiency
built environment
solutions
artificial neural networks
decision support system
quantitative and qualitative analysis
Issue Date: 2018
Publisher: MDPI
Citation: Kaklauskas, A.; Dzemyda, G.; Tupenaite L.; Voitau I.; Kurasova O.; Naimaviciene J.; Rassokha Y., Kanapeckiene L.2018.Artificial neural network-based decision support system for development of an energy-efficient built environment, MDPI11(8):1-20
Series/Report no.: 11;8
Abstract: Implementing energy-efficient solutions in a built environment is important for reaching international energy reduction targets. For advanced energy efficiency-related solutions, computer-based decision support systems are proposed and rapidly used in a variety of spheres relevant to a built environment. Present research proposes a novel artificial neural network-based decision support system for development of an energy-efficient built environment. The system was developed by integrating methods of the multiple criteria evaluation and multivariant design, determination of project utility and market value, and visual data mining by artificial neural networks. It enables a user to compose up to 100,000,000 combinations of the energy-efficient solutions, analyze strengths and weaknesses of a built environment projects, provide advice for stakeholders, and calculate market value and utility degree of the projects. For visual data mining, self-organizing maps (type neural networks) are used, which may influence the choosing of the final set of alternatives and criteria in the decision-making problem, taking into account the discovered similarities of alternatives or criteria. A system was validated by the real case study on the design of an energy-efficient individual house. Keywords: energy-efficiency; built environment; solutions; artificial neural networks; decision support system; quantitative and qualitative analysis
URI: http://dspace.vgtu.lt/handle/1/3743
ISSN: 1996-1073
Appears in Collections:Moksliniai straipsniai / Research articles

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