LAPSE:2018.0517
Published Article
LAPSE:2018.0517
Artificial Neural Network-Based Decision Support System for Development of an Energy-Efficient Built Environment
September 21, 2018
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.
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Keywords
artificial neural networks, built environment, decision support system, energy-efficiency, quantitative and qualitative analysis, solutions
Subject
Suggested Citation
Kaklauskas A, Dzemyda G, Tupenaite L, Voitau I, Kurasova O, Naimaviciene J, Rassokha Y, Kanapeckiene L. Artificial Neural Network-Based Decision Support System for Development of an Energy-Efficient Built Environment. (2018). LAPSE:2018.0517
Author Affiliations
Kaklauskas A: Department of Construction Management and Real Estate, Faculty of Civil Engineering, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania
Dzemyda G: Institute of Mathematics and Informatics, Vilnius University, Akademijos str. 4, LT-08663 Vilnius, Lithuania
Tupenaite L: Department of Construction Management and Real Estate, Faculty of Civil Engineering, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania
Voitau I: Belarusian State Technological University, Sverdlova str. 13a, 220006 Minsk, Belarus
Kurasova O: Institute of Mathematics and Informatics, Vilnius University, Akademijos str. 4, LT-08663 Vilnius, Lithuania
Naimaviciene J: Department of Construction Management and Real Estate, Faculty of Civil Engineering, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania
Rassokha Y: Belarusian State Technological University, Sverdlova str. 13a, 220006 Minsk, Belarus
Kanapeckiene L: Department of Construction Management and Real Estate, Faculty of Civil Engineering, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania
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Dzemyda G: Institute of Mathematics and Informatics, Vilnius University, Akademijos str. 4, LT-08663 Vilnius, Lithuania
Tupenaite L: Department of Construction Management and Real Estate, Faculty of Civil Engineering, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania
Voitau I: Belarusian State Technological University, Sverdlova str. 13a, 220006 Minsk, Belarus
Kurasova O: Institute of Mathematics and Informatics, Vilnius University, Akademijos str. 4, LT-08663 Vilnius, Lithuania
Naimaviciene J: Department of Construction Management and Real Estate, Faculty of Civil Engineering, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania
Rassokha Y: Belarusian State Technological University, Sverdlova str. 13a, 220006 Minsk, Belarus
Kanapeckiene L: Department of Construction Management and Real Estate, Faculty of Civil Engineering, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania
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Journal Name
Energies
Volume
11
Issue
8
Article Number
E1994
Year
2018
Publication Date
2018-08-01
ISSN
1996-1073
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Original Submission
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PII: en11081994, Publication Type: Journal Article
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LAPSE:2018.0517
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https://doi.org/10.3390/en11081994
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[v1] (Original Submission)
Sep 21, 2018
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Sep 21, 2018
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Calvin Tsay
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