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Building energy demand patterns for department stores in Korea

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  • Chung, Mo
  • Park, Hwa-Choon

Abstract

The daily energy demand patterns for 14 department stores located in the five largest cities in Korea are surveyed to establish building-energy load models. Electricity and fuel consumption data are field-measured at one carefully chosen store among the sample stores that best represent the statistical characteristics of the department stores to extract hourly consumption profiles for the electricity, heating, hot water, and cooling loads. The data statistically processed to develop a generic computational model for future applications.

Suggested Citation

  • Chung, Mo & Park, Hwa-Choon, 2012. "Building energy demand patterns for department stores in Korea," Applied Energy, Elsevier, vol. 90(1), pages 241-249.
    • Handle: RePEc:eee:appene:v:90:y:2012:i:1:p:241-249
    DOI: 10.1016/j.apenergy.2011.05.008
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    Cited by:

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    3. Young-Jik Youn & Yong-Hoon Im, 2022. "Technical Feasibility Assessment for a Novel Fifth-Generation District Heating Model of Interconnected Operation with a Large-Scale Building," Sustainability, MDPI, vol. 14(19), pages 1-30, October.
    4. Song, Jeonghun & Song, Seung Jin, 2020. "A framework for analyzing city-wide impact of building-integrated renewable energy," Applied Energy, Elsevier, vol. 276(C).
    5. Lim, Jae-Han & Song, Jin-Hee & Song, Seung-Yeong, 2014. "Development of operational guidelines for thermally activated building system according to heating and cooling load characteristics," Applied Energy, Elsevier, vol. 126(C), pages 123-135.
    6. Yonghoon Im, 2022. "Assessment of the Impact of Renewable Energy Expansion on the Technological Competitiveness of the Cogeneration Model," Energies, MDPI, vol. 15(18), pages 1-27, September.
    7. Apadula, Francesco & Bassini, Alessandra & Elli, Alberto & Scapin, Simone, 2012. "Relationships between meteorological variables and monthly electricity demand," Applied Energy, Elsevier, vol. 98(C), pages 346-356.
    8. Sergio Ortega Alba & Mario Manana, 2017. "Characterization and Analysis of Energy Demand Patterns in Airports," Energies, MDPI, vol. 10(1), pages 1-35, January.
    9. Manfren, Massimiliano & Aste, Niccolò & Moshksar, Reza, 2013. "Calibration and uncertainty analysis for computer models – A meta-model based approach for integrated building energy simulation," Applied Energy, Elsevier, vol. 103(C), pages 627-641.
    10. Mikkola, Jani & Lund, Peter D., 2014. "Models for generating place and time dependent urban energy demand profiles," Applied Energy, Elsevier, vol. 130(C), pages 256-264.
    11. Yeo, In-Ae & Yoon, Seong-Hwan & Yee, Jurng-Jae, 2013. "Development of an urban energy demand forecasting system to support environmentally friendly urban planning," Applied Energy, Elsevier, vol. 110(C), pages 304-317.
    12. Ferrari, Simone & Zagarella, Federica & Caputo, Paola & D'Amico, Antonino, 2019. "Results of a literature review on methods for estimating buildings energy demand at district level," Energy, Elsevier, vol. 175(C), pages 1130-1137.
    13. Youn, Young Jik & Im, Yong Hoon, 2023. "Assessment of the feasibility for hybrid seawater district cooling model under variable seawater temperature condition: Case study in South Korea," Renewable Energy, Elsevier, vol. 218(C).

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