Machine-learning-assisted long-term G functions for bidirectional aquifer thermal energy storage system operation

Author:

Chen Kecheng,Sun Xiang,Soga Kenichi,Nico Peter S.,Dobson Patrick F.ORCID

Funder

Office of Technology Development

Geothermal Technologies Office

National Science Foundation

Office of Energy Efficiency and Renewable Energy

U.S. Department of Energy

Lawrence Berkeley National Laboratory

Publisher

Elsevier BV

Reference113 articles.

1. Worldwide application of aquifer thermal energy storage–A review;Fleuchaus;Renew Sustain Energy Rev,2018

2. Thermodynamic analyses and assessments of various thermal energy storage systems for buildings;Caliskan;Energy Convers Manag,2012

3. A review of thermal energy storage technologies for seasonal loops;Mahon;Energy,2022

4. Office of Environmental Management. Technology readiness assessment guide,2011

5. Directive on the energy performance of buildings;Council,2010

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