IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i8p1994-d161168.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/8/1994/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/8/1994/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gintautas Dzemyda & Olga Kurasova & Julius Žilinskas, 2013. "Multidimensional Data Visualization," Springer Optimization and Its Applications, Springer, edition 127, number 978-1-4419-0236-8, September.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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).
    11. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Roya Etminani-Ghasrodashti & Chen Kan & Muhammad Arif Qaisrani & Omer Mogultay & Houliang Zhou, 2021. "Examining the Impacts of the Built Environment on Quality of Life in Cancer Patients Using Machine Learning," Sustainability, MDPI, vol. 13(10), pages 1-19, May.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arturas Kaklauskas & Edmundas Kazimieras Zavadskas & Bjoern Schuller & Natalija Lepkova & Gintautas Dzemyda & Jurate Sliogeriene & Olga Kurasova, 2020. "Customized ViNeRS Method for Video Neuro-Advertising of Green Housing," IJERPH, MDPI, vol. 17(7), pages 1-28, March.
    2. Dzemyda, Gintautas & Sabaliauskas, Martynas, 2021. "Geometric multidimensional scaling: A new approach for data dimensionality reduction," Applied Mathematics and Computation, Elsevier, vol. 409(C).
    3. Jurgita Markevičiūtė & Jolita Bernatavičienė & Rūta Levulienė & Viktor Medvedev & Povilas Treigys & Julius Venskus, 2022. "Impact of COVID-19-Related Lockdown Measures on Economic and Social Outcomes in Lithuania," Mathematics, MDPI, vol. 10(15), pages 1-20, August.
    4. Danutė Krapavickaitė, 2022. "Coherence Coefficient for Official Statistics," Mathematics, MDPI, vol. 10(7), pages 1-20, April.
    5. Mulliner, Emma & Smallbone, Kieran & Maliene, Vida, 2013. "An assessment of sustainable housing affordability using a multiple criteria decision making method," Omega, Elsevier, vol. 41(2), pages 270-279.
    6. Deqiang Cheng & Chunliu Gao, 2022. "Regionalization Research of Mountain-Hazards Developing Environments for the Eurasian Continent," Land, MDPI, vol. 11(9), pages 1-19, September.
    7. Xiao-Juan Li & Chen Wang & Wei-bin Chen & Shilpi Bora & Jeffrey Boon Hui Yap & Bimenyimana Samuel, 2022. "Green building performance assessment in China using a cloud model," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(10), pages 11626-11650, October.
    8. Harmsen - van Hout, Marjolein & Ghosh, Gaurav & Madlener, Reinhard, 2013. "The Impact of Green Framing on Consumers’ Valuations of Energy-Saving Measures," FCN Working Papers 7/2013, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    9. Cinzia Colapinto & Raja Jayaraman & Fouad Ben Abdelaziz & Davide La Torre, 2020. "Environmental sustainability and multifaceted development: multi-criteria decision models with applications," Annals of Operations Research, Springer, vol. 293(2), pages 405-432, October.
    10. Antonio Artino & Riccardo Caponetto & Gianpiero Evola & Giuseppe Margani & Edoardo Michele Marino & Emanuele Murgano, 2020. "Decision Support System for the Sustainable Seismic and Energy Renovation of Buildings: Methodological Layout," Sustainability, MDPI, vol. 12(24), pages 1-21, December.
    11. Qing He & Haiyang Zhao & Lin Shen & Liuqun Dong & Ye Cheng & Ke Xu, 2019. "Factors Influencing Residents’ Intention toward Green Retrofitting of Existing Residential Buildings," Sustainability, MDPI, vol. 11(15), pages 1-23, August.
    12. Dergiades, Theologos & Madlener, Reinhard & Christofidou, Georgia, 2018. "The nexus between natural gas spot and futures prices at NYMEX: Do weather shocks and non-linear causality in low frequencies matter?," The Journal of Economic Asymmetries, Elsevier, vol. 18(C), pages 1-1.
    13. Wang, Xiaotong & Lu, Meijun & Mao, Wei & Ouyang, Jinlong & Zhou, Bo & Yang, Yunkai, 2015. "Improving benefit-cost analysis to overcome financing difficulties in promoting energy-efficient renovation of existing residential buildings in China," Applied Energy, Elsevier, vol. 141(C), pages 119-130.
    14. Haonan Zhang, 2023. "Leveraging policy instruments and financial incentives to reduce embodied carbon in energy retrofits," Papers 2304.03403, arXiv.org.
    15. Aliakbar Kamari & Ashwin Paari & Henrik Øien Torvund, 2020. "BIM-Enabled Virtual Reality (VR) for Sustainability Life Cycle and Cost Assessment," Sustainability, MDPI, vol. 13(1), pages 1-24, December.
    16. Gliedt, Travis & Hoicka, Christina E., 2015. "Energy upgrades as financial or strategic investment? Energy Star property owners and managers improving building energy performance," Applied Energy, Elsevier, vol. 147(C), pages 430-443.
    17. Radhi, Hassan, 2012. "Trade-off between environmental and economic implications of PV systems integrated into the UAE residential sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 2468-2474.
    18. Işık, Erdem & Inallı, Mustafa, 2018. "Artificial neural networks and adaptive neuro-fuzzy inference systems approaches to forecast the meteorological data for HVAC: The case of cities for Turkey," Energy, Elsevier, vol. 154(C), pages 7-16.
    19. Luigi Dolores & Maria Macchiaroli & Gianluigi De Mare, 2022. "Financial Impacts of the Energy Transition in Housing," Sustainability, MDPI, vol. 14(9), pages 1-17, April.
    20. Aurora Greta Ruggeri & Laura Gabrielli & Massimiliano Scarpa, 2020. "Energy Retrofit in European Building Portfolios: A Review of Five Key Aspects," Sustainability, MDPI, vol. 12(18), pages 1-38, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:1994-:d:161168. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.