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A decision support model for reducing electric energy consumption in elementary school facilities

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  • Hong, Taehoon
  • Koo, Choongwan
  • Jeong, Kwangbok

Abstract

The South Korean government has been actively promoting an educational-facility improvement program as part of its energy-saving efforts. This research seeks to develop a decision support model for selecting the facility expected to be effective in generating energy savings and making the facility improvement program more effective. In this research, project characteristics and electric-energy consumption data for the year 2009 were collected from 6282 elementary schools located in seven metropolitan cities in South Korea. In this research, the following were carried out: (i) a group of educational facilities was established based on electric-energy consumption, using a decision tree; (ii) a number of similar projects were retrieved from the same group of facilities, using case-based reasoning; and (iii) the accuracy of prediction was improved, using the combination of genetic algorithms, the artificial neural network, and multiple regression analysis. The results of this research can be useful for the following purposes: (i) preliminary research on the systematic and continuous management of educational facilities’ electric-energy consumption; (ii) basic research on electric-energy consumption prediction based on the project characteristics; and (iii) practical research for selecting an optimum facility that can more effectively apply an educational-facility improvement program as a decision support model.

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  • Hong, Taehoon & Koo, Choongwan & Jeong, Kwangbok, 2012. "A decision support model for reducing electric energy consumption in elementary school facilities," Applied Energy, Elsevier, vol. 95(C), pages 253-266.
    • Handle: RePEc:eee:appene:v:95:y:2012:i:c:p:253-266
    DOI: 10.1016/j.apenergy.2012.02.052
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    Cited by:

    1. Jeong, Kwangbok & Hong, Taehoon & Kim, Jimin & Cho, Kyuman, 2019. "Development of a multi-objective optimization model for determining the optimal CO2 emissions reduction strategies for a multi-family housing complex," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 118-131.
    2. Hong, Taehoon & Jeong, Kwangbok & Koo, Choongwan, 2018. "An optimized gene expression programming model for forecasting the national CO2 emissions in 2030 using the metaheuristic algorithms," Applied Energy, Elsevier, vol. 228(C), pages 808-820.
    3. Jeong, Kwangbok & Hong, Taehoon & Kim, Jimin & Lee, Jaewook, 2021. "A data-driven approach for establishing a CO2 emission benchmark for a multi-family housing complex using data mining techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    4. Koo, Choongwan & Kim, Hyunjoong & Hong, Taehoon, 2014. "Framework for the analysis of the low-carbon scenario 2020 to achieve the national carbon Emissions reduction target: Focused on educational facilities," Energy Policy, Elsevier, vol. 73(C), pages 356-367.
    5. Raatikainen, Mika & Skön, Jukka-Pekka & Leiviskä, Kauko & Kolehmainen, Mikko, 2016. "Intelligent analysis of energy consumption in school buildings," Applied Energy, Elsevier, vol. 165(C), pages 416-429.
    6. Koo, Choongwan & Hong, Taehoon, 2015. "Development of a dynamic operational rating system in energy performance certificates for existing buildings: Geostatistical approach and data-mining technique," Applied Energy, Elsevier, vol. 154(C), pages 254-270.
    7. Jeong, Kwangbok & Koo, Choongwan & Hong, Taehoon, 2014. "An estimation model for determining the annual energy cost budget in educational facilities using SARIMA (seasonal autoregressive integrated moving average) and ANN (artificial neural network)," Energy, Elsevier, vol. 71(C), pages 71-79.
    8. Jeong, Jaewook & Hong, Taehoon & Ji, Changyoon & Kim, Jimin & Lee, Minhyun & Jeong, Kwangbok, 2016. "Development of an integrated energy benchmark for a multi-family housing complex using district heating," Applied Energy, Elsevier, vol. 179(C), pages 1048-1061.
    9. Hong, Taehoon & Koo, Choongwan & Lee, Sungug, 2014. "Benchmarks as a tool for free allocation through comparison with similar projects: Focused on multi-family housing complex," Applied Energy, Elsevier, vol. 114(C), pages 663-675.
    10. Koo, Choongwan & Hong, Taehoon & Lee, Minhyun & Seon Park, Hyo, 2014. "Development of a new energy efficiency rating system for existing residential buildings," Energy Policy, Elsevier, vol. 68(C), pages 218-231.
    11. Koo, Choongwan & Hong, Taehoon & Jeong, Kwangbok & Ban, Cheolwoo & Oh, Jeongyoon, 2017. "Development of the smart photovoltaic system blind and its impact on net-zero energy solar buildings using technical-economic-political analyses," Energy, Elsevier, vol. 124(C), pages 382-396.
    12. Kim, Jimin & Hong, Taehoon & Jeong, Jaemin & Koo, Choongwan & Jeong, Kwangbok, 2016. "An optimization model for selecting the optimal green systems by considering the thermal comfort and energy consumption," Applied Energy, Elsevier, vol. 169(C), pages 682-695.
    13. Koo, Choongwan & Hong, Taehoon & Kim, Jimin & Kim, Hyunjoong, 2015. "An integrated multi-objective optimization model for establishing the low-carbon scenario 2020 to achieve the national carbon emissions reduction target for residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 410-425.
    14. Michele Zinzi & Francesca Pagliaro & Stefano Agnoli & Fabio Bisegna & Domenico Iatauro, 2021. "On the Built-Environment Quality in Nearly Zero-Energy Renovated Schools: Assessment and Impact of Passive Strategies," Energies, MDPI, vol. 14(10), pages 1-18, May.
    15. Kim, Jimin & Hong, Taehoon & Jeong, Jaemin & Lee, Myeonghwi & Koo, Choongwan & Lee, Minhyun & Ji, Changyoon & Jeong, Jaewook, 2016. "An integrated multi-objective optimization model for determining the optimal solution in the solar thermal energy system," Energy, Elsevier, vol. 102(C), pages 416-426.
    16. Jeong, Jaewook & Hong, Taehoon & Ji, Changyoon & Kim, Jimin & Lee, Minhyun & Jeong, Kwangbok & Koo, Choongwan, 2017. "Development of a prediction model for the cost saving potentials in implementing the building energy efficiency rating certification," Applied Energy, Elsevier, vol. 189(C), pages 257-270.
    17. Muhammad Waseem Ahmad & Anthony Mouraud & Yacine Rezgui & Monjur Mourshed, 2018. "Deep Highway Networks and Tree-Based Ensemble for Predicting Short-Term Building Energy Consumption," Energies, MDPI, vol. 11(12), pages 1-21, December.
    18. Murray, Alan T., 2016. "Assessing the impacts of traditional school year calendar start dates," Socio-Economic Planning Sciences, Elsevier, vol. 54(C), pages 28-36.
    19. Hong, Taehoon & Koo, Choongwan & Kim, Daeho & Lee, Minhyun & Kim, Jimin, 2015. "An estimation methodology for the dynamic operational rating of a new residential building using the advanced case-based reasoning and stochastic approaches," Applied Energy, Elsevier, vol. 150(C), pages 308-322.
    20. Paola Marrone & Paola Gori & Francesco Asdrubali & Luca Evangelisti & Laura Calcagnini & Gianluca Grazieschi, 2018. "Energy Benchmarking in Educational Buildings through Cluster Analysis of Energy Retrofitting," Energies, MDPI, vol. 11(3), pages 1-20, March.

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