IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i21p5796-d440555.html
   My bibliography  Save this article

Statistical Methodology for the Definition of Standard Model for Energy Analysis of Residential Buildings in Korea

Author

Listed:
  • Hye-Ryeong Nam

    (Energy ICT Convergence Research Department, Korea Institute of Energy Research, Daejeon 34101, Korea
    School of Architectural, Civil, Environmental, and Energy Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Korea)

  • Seo-Hoon Kim

    (Energy ICT Convergence Research Department, Korea Institute of Energy Research, Daejeon 34101, Korea)

  • Seol-Yee Han

    (Energy ICT Convergence Research Department, Korea Institute of Energy Research, Daejeon 34101, Korea)

  • Sung-Jin Lee

    (Energy ICT Convergence Research Department, Korea Institute of Energy Research, Daejeon 34101, Korea)

  • Won-Hwa Hong

    (School of Architectural, Civil, Environmental, and Energy Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Korea)

  • Jong-Hun Kim

    (Energy ICT Convergence Research Department, Korea Institute of Energy Research, Daejeon 34101, Korea)

Abstract

This study was conducted to propose an optimal methodology for deriving a standard model from existing residential buildings. To strategically improve existing residential buildings, it is necessary to identify standard models that can be used as quantitative standards. In this study, a total of six methods were established for different algorithms in the dimensionality reduction and clustering stage of the data preprocessing stage. In addition, a total of 22,342 households’ data were analyzed, and a total of 26 variables were used to perform cluster analysis. The process of method 6 (data pre-processing, principal components analysis, clustering [K-medoids], verification) was proposed as a way to derive the standard model from the existing Korean housing. The method proposed in this study is capable of deriving a number of standard models considering all variables (n) in a single analysis. The representative building derived in this study contains a lot of building data, so it can be effectively used for planning and research related to buildings on a regional and national scale. In addition, this process can be applied to various buildings to derive representative buildings.

Suggested Citation

  • Hye-Ryeong Nam & Seo-Hoon Kim & Seol-Yee Han & Sung-Jin Lee & Won-Hwa Hong & Jong-Hun Kim, 2020. "Statistical Methodology for the Definition of Standard Model for Energy Analysis of Residential Buildings in Korea," Energies, MDPI, vol. 13(21), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5796-:d:440555
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/21/5796/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/21/5796/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Corgnati, Stefano Paolo & Fabrizio, Enrico & Filippi, Marco & Monetti, Valentina, 2013. "Reference buildings for cost optimal analysis: Method of definition and application," Applied Energy, Elsevier, vol. 102(C), pages 983-993.
    2. Osama Omar, 2020. "Near Zero-Energy Buildings in Lebanon: The Use of Emerging Technologies and Passive Architecture," Sustainability, MDPI, vol. 12(6), pages 1-13, March.
    3. Seo-Hoon Kim & Jung-Hun Lee & Jong-Hun Kim & Seung-Hwan Yoo & Hak-Geun Jeong, 2018. "The Feasibility of Improving the Accuracy of In Situ Measurements in the Air-Surface Temperature Ratio Method," Energies, MDPI, vol. 11(7), pages 1-18, July.
    4. Maria-Mar Fernandez-Antolin & José-Manuel del-Río & Roberto-Alonso Gonzalez-Lezcano, 2019. "Influence of Solar Reflectance and Renewable Energies on Residential Heating and Cooling Demand in Sustainable Architecture: A Case Study in Different Climate Zones in Spain Considering Their Urban Co," Sustainability, MDPI, vol. 11(23), pages 1-31, November.
    5. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    6. Seo-Hoon Kim & Jong-Hun Kim & Hak-Geun Jeong & Kyoo-Dong Song, 2018. "Reliability Field Test of the Air–Surface Temperature Ratio Method for In Situ Measurement of U-Values," Energies, MDPI, vol. 11(4), pages 1-15, March.
    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. Iasmin Lourenço Niza & Evandro Eduardo Broday, 2022. "An Analysis of Thermal Comfort Models: Which One Is Suitable Model to Assess Thermal Reality in Brazil?," Energies, MDPI, vol. 15(15), pages 1-19, July.

    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. Bienvenido-Huertas, David & Moyano, Juan & Rodríguez-Jiménez, Carlos E. & Marín, David, 2019. "Applying an artificial neural network to assess thermal transmittance in walls by means of the thermometric method," Applied Energy, Elsevier, vol. 233, pages 1-14.
    2. David Bienvenido-Huertas, 2020. "Assessing the Environmental Impact of Thermal Transmittance Tests Performed in Façades of Existing Buildings: The Case of Spain," Sustainability, MDPI, vol. 12(15), pages 1-18, August.
    3. Marianna Rotilio & Federica Cucchiella & Pierluigi De Berardinis & Vincenzo Stornelli, 2018. "Thermal Transmittance Measurements of the Historical Masonries: Some Case Studies," Energies, MDPI, vol. 11(11), pages 1-18, November.
    4. Iole Nardi & Elena Lucchi, 2023. "In Situ Thermal Transmittance Assessment of the Building Envelope: Practical Advice and Outlooks for Standard and Innovative Procedures," Energies, MDPI, vol. 16(8), pages 1-31, April.
    5. Prabal Das & D. A. Sachindra & Kironmala Chanda, 2022. "Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6043-6071, December.
    6. Jie Zhao & Ji Chen & Damien Beillouin & Hans Lambers & Yadong Yang & Pete Smith & Zhaohai Zeng & Jørgen E. Olesen & Huadong Zang, 2022. "Global systematic review with meta-analysis reveals yield advantage of legume-based rotations and its drivers," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    7. Piaopiao Chen & Agnès H. Michel & Jianzhi Zhang, 2022. "Transposon insertional mutagenesis of diverse yeast strains suggests coordinated gene essentiality polymorphisms," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    8. Paulo Infante & Gonçalo Jacinto & Anabela Afonso & Leonor Rego & Pedro Nogueira & Marcelo Silva & Vitor Nogueira & José Saias & Paulo Quaresma & Daniel Santos & Patrícia Góis & Paulo Rebelo Manuel, 2023. "Factors That Influence the Type of Road Traffic Accidents: A Case Study in a District of Portugal," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    9. Ephrem Habyarimana & Faheem S Baloch, 2021. "Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-23, March.
    10. Banks, Jonathan & Rabbani, Arif & Nadkarni, Kabir & Renaud, Evan, 2020. "Estimating parasitic loads related to brine production from a hot sedimentary aquifer geothermal project: A case study from the Clarke Lake gas field, British Columbia," Renewable Energy, Elsevier, vol. 153(C), pages 539-552.
    11. Crespo, Cristian, 2020. "Two become one: improving the targeting of conditional cash transfers with a predictive model of school dropout," LSE Research Online Documents on Economics 123139, London School of Economics and Political Science, LSE Library.
    12. Alexander Wettstein & Gabriel Jenni & Ida Schneider & Fabienne Kühne & Martin grosse Holtforth & Roberto La Marca, 2023. "Predictors of Psychological Strain and Allostatic Load in Teachers: Examining the Long-Term Effects of Biopsychosocial Risk and Protective Factors Using a LASSO Regression Approach," IJERPH, MDPI, vol. 20(10), pages 1-20, May.
    13. Tang, Kayu & Parsons, David J. & Jude, Simon, 2019. "Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 24-36.
    14. Daifeng Xiang & Gangsheng Wang & Jing Tian & Wanyu Li, 2023. "Global patterns and edaphic-climatic controls of soil carbon decomposition kinetics predicted from incubation experiments," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    15. Joel Podgorski & Oliver Kracht & Luis Araguas-Araguas & Stefan Terzer-Wassmuth & Jodie Miller & Ralf Straub & Rolf Kipfer & Michael Berg, 2024. "Groundwater vulnerability to pollution in Africa’s Sahel region," Nature Sustainability, Nature, vol. 7(5), pages 558-567, May.
    16. Michele Roccotelli & Alessandro Rinaldi & Maria Pia Fanti & Francesco Iannone, 2020. "Building Energy Management for Passive Cooling Based on Stochastic Occupants Behavior Evaluation," Energies, MDPI, vol. 14(1), pages 1-24, December.
    17. Bellotti, Anthony & Brigo, Damiano & Gambetti, Paolo & Vrins, Frédéric, 2021. "Forecasting recovery rates on non-performing loans with machine learning," International Journal of Forecasting, Elsevier, vol. 37(1), pages 428-444.
    18. Tranos, Emmanouil & Incera, Andre Carrascal & Willis, George, 2022. "Using the web to predict regional trade flows: data extraction, modelling, and validation," OSF Preprints 9bu5z, Center for Open Science.
    19. Belen Moreno Santamaria & Fernando del Ama Gonzalo & Benito Lauret Aguirregabiria & Juan A. Hernandez Ramos, 2020. "Experimental Validation of Water Flow Glazing: Transient Response in Real Test Rooms," Sustainability, MDPI, vol. 12(14), pages 1-24, July.
    20. Baglivo, Cristina & Congedo, Paolo Maria & D'Agostino, Delia & Zacà, Ilaria, 2015. "Cost-optimal analysis and technical comparison between standard and high efficient mono-residential buildings in a warm climate," Energy, Elsevier, vol. 83(C), pages 560-575.

    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:13:y:2020:i:21:p:5796-:d:440555. 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.