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Project
Improving Aquifer Thermal Energy Storage Systems Design through Advanced Hydrogeological Uncertainty Quantification (ATES2.0)
Shallow geothermal energy is a sustainable alternative to provide heating or cooling to buildings. The objective of this research is to improve the design of shallow geothermal systems and predict the uncertainty of their energy efficiency using a new stochastic framework called Bayesian evidential learning (BEL). The method will be validated on two in-use aquifer thermal energy storage systems.
Date:1 Nov 2022 → 31 Oct 2023
Keywords:Bayesian Evidential Learning, Uncertainty quantification, Aquifer thermal energy storage (ATES) system
Disciplines:Renewable power and energy systems engineering, Geology not elsewhere classified, Geothermal energy, Energy storage, Hydrogeology