Development of the hybrid model for estimating the undisturbed ground temperature using the finite element method and geostatistical technique

Kwangbok Jeong, Taehoon Hong, Myeongsoo Chae, Jimin Kim, Minhyun Lee, Choongwan Koo, Changyoon Ji

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Shallow geothermal potential needs to be evaluated when introducing the ground source heat pump (GSHP) system. Accurate evaluation of shallow geothermal potential can be done by estimating underground thermal properties with precision. The data previously established from measuring underground thermal properties, however, have limitations in that the locations where the measurements were made were not identical. Therefore, this study aimed to develop a hybrid model for estimating UGT using the finite element method (FEM) and the kriging method. This study was conducted in three steps: (i) data collection; (ii) estimation of the UGT in 216 non-measurement locations using FEM and the kriging method; and (iii) estimation of the shallow geothermal potential using the geothermal potential (G.POT) method. The average mean absolute percentage error of the hybrid model (3.67%) was lower than those of the other estimation models using FEM and the kriging method (FEM: 4.62%; Kriging method: 6.34%). Thus, the analysis showed that the developed hybrid model was the most suitable method for estimating UGT in all the length ranges (65–250 m) where the ground heat exchanger was to be installed. The hybrid model can help policy makers evaluate shallow geothermal potential at the national level.

Original languageEnglish
Pages (from-to)162-174
Number of pages13
JournalEnergy and Buildings
Volume152
DOIs
Publication statusPublished - 2017 Oct 1

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP; Ministry of Science, ICT & Future Planning) (No. NRF-2015R1A2A1A05001657).

Publisher Copyright:
© 2017 Elsevier B.V.

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction
  • Mechanical Engineering
  • Electrical and Electronic Engineering

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