Optimizing Geothermal Power Plants Using Artificial Intelligence

Authors: Jay GOHIL, Preyansh MALAVIYA, Vishwadeepsinh SARVAIYA, Manan SHAH,
Keywords: Optimization, Power Plants, Artificial Intelligence
Conference: World Geothermal Congress Session: Software for Geothermal Applications
Year: 2020 Language: English
Abstract: The conventional methods of generating electricity have a huge side effect on the global climate. Renewable energy is a viable solution, with the benefits of minimal carbon pollution, to make the world safer and more energy-efficient. Over the past few years, geothermal energy is the one which has been exploited and this innovation has brought down the cost. The effectiveness of the application of geothermal energy typically depends on the ability of the load to extract sufficiently high energy from the earth to maintain high energy conversion efficiency. However, the incorporation of geothermal energy into the grid is regulated by a good number of nonlinear interactions across multiple parameters. Advancements in Artificial Intelligence (AI) develop smart entities that produce more precise predictions for complex issues. AI algorithms (e.g. neural networks, deep learning, fuzzy logic, and intelligent optimization algorithms) have become extremely prevalent in solving problems such as optimization, supply prediction, forecasting, and modeling. Today’s geothermal industries rely heavily on humans to determine the key characteristics that make for ideal geothermal prospects. But, via machine learning and Artificial intelligence, scientists strive to significantly enhance geothermal exploration-computer programs that can process vast quantities of data, learn from it, and then adjust their algorithms automatically to process it with increasing precision and performance. The integration of Artificial intelligence and machine learning with the geothermal sector can help us to overcome certain challenges and optimize the techniques used to extract it. This includes improvements in energy operations, system reliabilities, line loss predictions, predicting equipment failure and load forecasting, and parameter estimations. This paper delves to contribute to improving and propelling the extraction capacity, optimizing processes, integration of geothermal energy into the grid, and efficiency with AI technologies. We are interested in reviews based on innovative AI applications for geothermal energy forecasting and analysis that are theoretically, empirically, and methodologically driven, including reviews and case studies
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