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• Sustainable Geothermal Energy for the Future: AI in ATES

  ATES-Heat Pump systems enable simultaneous supply of heating and cooling and provide free heating and cooling making them economical and thermally efficient solutions. However, suboptimal operation significantly hinders performance. Current modeling practices require extraordinary effort, domain knowledge and long computation time making it infeasible for control and operation of real systems. In this project, numerical and physics-informed machine learning(PIML) models are developed for ATES leveraging PIML ability to incorporate the physical laws governing a system in the learning process, utilizing their effectiveness in solving realistic problems and fast computation time. The models are tested on a comprehensively monitored (since 2016) ATES site and facilitate its integration into the control and operation system. The project aims to facilitate geothermal storage technology development and increase Sweden's research and industry competitiveness regarding design, modeling, operation, and control.