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Artificial Neural Network-Based Decision Support System for Development of an Energy-Efficient Built Environment

Author

Listed:
  • Arturas Kaklauskas

    (Department of Construction Management and Real Estate, Faculty of Civil Engineering, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania)

  • Gintautas Dzemyda

    (Institute of Mathematics and Informatics, Vilnius University, Akademijos str. 4, LT-08663 Vilnius, Lithuania)

  • Laura Tupenaite

    (Department of Construction Management and Real Estate, Faculty of Civil Engineering, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania)

  • Ihar Voitau

    (Belarusian State Technological University, Sverdlova str. 13a, 220006 Minsk, Belarus)

  • Olga Kurasova

    (Institute of Mathematics and Informatics, Vilnius University, Akademijos str. 4, LT-08663 Vilnius, Lithuania)

  • Jurga Naimaviciene

    (Department of Construction Management and Real Estate, Faculty of Civil Engineering, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania)

  • Yauheni Rassokha

    (Belarusian State Technological University, Sverdlova str. 13a, 220006 Minsk, Belarus)

  • Loreta Kanapeckiene

    (Department of Construction Management and Real Estate, Faculty of Civil Engineering, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania)

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.

Suggested Citation

  • Arturas Kaklauskas & Gintautas Dzemyda & Laura Tupenaite & Ihar Voitau & Olga Kurasova & Jurga Naimaviciene & Yauheni Rassokha & Loreta Kanapeckiene, 2018. "Artificial Neural Network-Based Decision Support System for Development of an Energy-Efficient Built Environment," Energies, MDPI, vol. 11(8), pages 1-20, August.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:1994-:d:161168
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    References listed on IDEAS

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    1. Nikolaidis, Yiannis & Pilavachi, Petros A. & Chletsis, Alexandros, 2009. "Economic evaluation of energy saving measures in a common type of Greek building," Applied Energy, Elsevier, vol. 86(12), pages 2550-2559, December.
    2. Gintautas Dzemyda & Olga Kurasova & Julius Žilinskas, 2013. "Applications of Visualization," Springer Optimization and Its Applications, in: Multidimensional Data Visualization, edition 127, chapter 0, pages 179-226, Springer.
    3. Dzemyda, Gintautas & Kurasova, Olga, 2006. "Heuristic approach for minimizing the projection error in the integrated mapping," European Journal of Operational Research, Elsevier, vol. 171(3), pages 859-878, June.
    4. Kumbaroğlu, Gürkan & Madlener, Reinhard, 2011. "Evaluation of Economically Optimal Retrofit Investment Options for Energy Savings in Buildings," FCN Working Papers 14/2011, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    5. Patrizia Lombardi & Francesca Abastante & Sara Torabi Moghadam & Jacopo Toniolo, 2017. "Multicriteria Spatial Decision Support Systems for Future Urban Energy Retrofitting Scenarios," Sustainability, MDPI, vol. 9(7), pages 1-13, July.
    6. Gintautas Dzemyda & Olga Kurasova & Julius Žilinskas, 2013. "Multidimensional Data Visualization," Springer Optimization and Its Applications, Springer, edition 127, number 978-1-4419-0236-8, December.
    7. Bernataviciene, Jolita & Dzemyda, Gintautas & Kurasova, Olga & Marcinkevicius, Virginijus, 2006. "Optimal decisions in combining the SOM with nonlinear projection methods," European Journal of Operational Research, Elsevier, vol. 173(3), pages 729-745, September.
    8. Khayatian, Fazel & Sarto, Luca & Dall'O', Giuliano, 2017. "Building energy retrofit index for policy making and decision support at regional and national scales," Applied Energy, Elsevier, vol. 206(C), pages 1062-1075.
    9. Banaitiene, Nerija & Banaitis, Audrius & Kaklauskas, Arturas & Zavadskas, Edmundas Kazimieras, 2008. "Evaluating the life cycle of a building: A multivariant and multiple criteria approach," Omega, Elsevier, vol. 36(3), pages 429-441, June.
    10. Beccali, Marco & Ciulla, Giuseppina & Lo Brano, Valerio & Galatioto, Alessandra & Bonomolo, Marina, 2017. "Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in Southern Italy," Energy, Elsevier, vol. 137(C), pages 1201-1218.
    11. Aliakbar Kamari & Stefan Jensen & Maria Leonhard Christensen & Steffen Petersen & Poul Henning Kirkegaard, 2018. "A hybrid Decision Support System for Generation of Holistic Renovation Scenarios—Cases of Energy Consumption, Investment Cost, and Thermal Indoor Comfort," Sustainability, MDPI, vol. 10(4), pages 1-23, April.
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    2. Sławomir Francik & Adrian Knapczyk & Artur Knapczyk & Renata Francik, 2020. "Decision Support System for the Production of Miscanthus and Willow Briquettes," Energies, MDPI, vol. 13(6), pages 1-24, March.

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