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Suitability evaluation system for the shallow geothermal energy implementation in region by Entropy Weight Method and TOPSIS method

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  • Li, Zhao
  • Luo, Zujiang
  • Wang, Yan
  • Fan, Guanyu
  • Zhang, Jianmang

Abstract

Shallow geothermal energy suitability map presents the potential for implementation in a region. The potential for implementation depends on hydrogeology, geotechnical, geology environment, and geothermal characteristics. Plenty of scholars evaluate shallow geothermal energy by the algorithm combined Analytic Hierarchy Process and Index Overlap. But Analytic Hierarchy Process and Index Overlap, as knowledge driven methods, rely on the experts' experience. This research presents a data driven algorithm based on Entropy Weight Method and TOPSIS Method. The weights are calculated by the Entropy Weight Method and assigned to the TOPSIS model. The closeness coefficient could be calculated by TOPSIS model. The suitability potential is analysed by comparing the closeness coefficient. The algorithm is accomplished by coding a program using Matlab. The algorithm is also applied to Nantong, China. Depending on the principle of ground source heat pump system, the suitability evaluation system of the open loop system and the closed loop system are established, respectively. Hydrogeology, geotechnical, geothermal, and geology environmental investigations are carried out to obtain the measured data and parameters for suitability analysis. The suitability maps are drawn in according with closeness coefficient. The algorithm is able to overcome the subjectivity of experts' experience. Compared with knowledge driven methods, the proposed algorithm tends to compare the relative potential in a region, rather than assess whether the site is suitable for SGE implementation. Consequently, it is more suitable for selecting the best field-site.

Suggested Citation

  • Li, Zhao & Luo, Zujiang & Wang, Yan & Fan, Guanyu & Zhang, Jianmang, 2022. "Suitability evaluation system for the shallow geothermal energy implementation in region by Entropy Weight Method and TOPSIS method," Renewable Energy, Elsevier, vol. 184(C), pages 564-576.
    • Handle: RePEc:eee:renene:v:184:y:2022:i:c:p:564-576
    DOI: 10.1016/j.renene.2021.11.112
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