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Development of a Mobile Application for Building Energy Prediction Using Performance Prediction Model

Author

Listed:
  • Yu-Ri Kim

    (Department of Architecture, Chung-Ang University, 84 Heuksoek-ro, Dongjak-gu, Seoul 06974, Korea)

  • Hae Jin Kang

    (Sustainable Design Team, SAMOO Architects and Engineers, 295 Olympic-ro, Songpa-gu, Seoul 05510, Korea)

Abstract

Recently, the Korean government has enforced disclosure of building energy performance, so that such information can help owners and prospective buyers to make suitable investment plans. Such a building energy performance policy of the government makes it mandatory for the building owners to obtain engineering audits and thereby evaluate the energy performance levels of their buildings. However, to calculate energy performance levels ( i.e. , asset rating methodology), a qualified expert needs to have access to at least the full project documentation and/or conduct an on-site inspection of the buildings. Energy performance certification costs a lot of time and money. Moreover, the database of certified buildings is still actually quite small. A need, therefore, is increasing for a simplified and user-friendly energy performance prediction tool for non-specialists. Also, a database which allows building owners and users to compare best practices is required. In this regard, the current study developed a simplified performance prediction model through experimental design, energy simulations and ANOVA (analysis of variance). Furthermore, using the new prediction model, a related mobile application was also developed.

Suggested Citation

  • Yu-Ri Kim & Hae Jin Kang, 2016. "Development of a Mobile Application for Building Energy Prediction Using Performance Prediction Model," Energies, MDPI, vol. 9(3), pages 1-16, March.
  • Handle: RePEc:gam:jeners:v:9:y:2016:i:3:p:160-:d:65105
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    Cited by:

    1. Roberto Zanetti Freire & Gerson Henrique dos Santos & Leandro dos Santos Coelho, 2017. "Hygrothermal Dynamic and Mould Growth Risk Predictions for Concrete Tiles by Using Least Squares Support Vector Machines," Energies, MDPI, vol. 10(8), pages 1-16, July.

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