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

Yu-Ri Kim and Hae Jin Kang
Additional contact information
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

Energies, 2016, vol. 9, issue 3, 1-16

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.

Keywords: energy performance certification; prediction model; mobile application; energy simulation; analysis of variance (ANOVA) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/9/3/160/pdf (application/pdf)
https://www.mdpi.com/1996-1073/9/3/160/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:9:y:2016:i:3:p:160-:d:65105

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().