The energy production potential of renewable energy sources is expanding rapidly, and increased CO2 emissions in the atmosphere indicate the importance of using environmentally friendly energy production options. Although these clean energy options offer substantial solutions for a better world, the energy efficiency of most renewable energy systems is still not competitive with other energy sources, and the systems need to be improved. Artificial intelligence (AI) technologies have begun to be applied in various areas and can be a good solution to increase the existing system efficiency of power production from renewables. Geothermal energy is one of the major players among renewables; however, it is costly and a risky investment, and it requires comprehensive resources and plant management. AI technologies can be integrated to the geothermal energy systems and these new technologies to increase the system efficiency and data management effectiveness and sustainability by applying correct methods for optimization and control systems with different AI algorithms both geothermal reservoir and the power plant parts especially in complex geothermal power systems such as flash, multi-flash and advanced geothermal power cycles. The present study proposed that to apply digital twin in geothermal power systems to increase the plant efficiency and to extend the lifetime of power plant equipment such as reinjection pump, which is quite critical for reinjection application in geothermal power plants. This paper mainly focuses on the predictive maintenance part of a collaborative effort to build a digital twin of reinjection pumps in a geothermal power plant.

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