An Intelligent Long Short-Term Memory-Based Machine Learning Model for the Potential Assessment of Global Hydropower Capacity in Sustainable Energy Transition and Security

Muhammad Amir Raza, Abdul Karim, Mohammed Alqarni (), Mahmoud Ahmad Al-Khasawneh, Touqeer Ahmed Jumani, Mohammed Aman and Muhammad I. Masud
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Muhammad Amir Raza: Department of Electrical Engineering, Mehran University of Engineering and Technology, SZAB Campus, Khairpur Mir’s 66020, Pakistan
Abdul Karim: Department of Electrical Engineering, Mehran University of Engineering and Technology, SZAB Campus, Khairpur Mir’s 66020, Pakistan
Mohammed Alqarni: Department of Electrical Engineering, College of Engineering, University of Business and Technology, Jeddah 21361, Saudi Arabia
Mahmoud Ahmad Al-Khasawneh: Hourani Centre for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan
Touqeer Ahmed Jumani: Department of Electrical Engineering and Computer Science, College of Engineering, A’Sharqiyah University, Ibra 400, Oman
Mohammed Aman: Department of Industrial Engineering, College of Engineering, University of Business and Technology, Jeddah 21361, Saudi Arabia
Muhammad I. Masud: Department of Electrical Engineering, College of Engineering, University of Business and Technology, Jeddah 21361, Saudi Arabia

Energies, 2025, vol. 18, issue 13, 1-27

Abstract: Climate change is a pressing global issue with severe consequences for the planet and human health. The Earth’s temperature has risen by 2 °C from 1901 to 2023, and this warming trend is expected to continue, causing potentially dangerous shifts in climate. Climate change impacts are already visible, with more frequent and severe heat waves, droughts, intense rain, and floods becoming increasingly common. Therefore, hydropower can contribute to addressing the global climate change issue and help to achieve global energy transition and stabilize global energy security. A Long Short-Term Memory (LSTM)-based model implemented in Python for global and regional hydropower forecasting was developed for a study period of 2023 to 2060 by taking the input data from 1980 to 2022. The results revealed that Asian countries have greater hydropower potential, which is expected to increase from 1926.51 TWh in 2023 to 2318.78 TWh in 2030, 2772.06 TWh in 2040, 2811.41 TWh in 2050, and 3195.79 TWh in 2060, as compared with the other regions of the world like the Middle East, Africa, Asia, Common Wealth of Independent State (CIS), Europe, North America, and South and Central America. The global hydropower potential is also expected to increase from 4350.12 TWh in 2023 to 4806.26 TWh in 2030, 5393.80 TWh in 2040, 6003.53 TWh in 2050, and 6644.06 TWh in 2060, which is sufficient for achieving energy transition and energy security goals. Furthermore, the performance and accuracy of the LSTM-based model were found to be 98%. This study will help in the efficient scheduling and management of hydropower resources, reducing uncertainties caused by environmental variability such as precipitation and runoff. The proposed model contributes to the energy transition and security that is needed to meet the global climate targets.

Keywords: global hydropower; climate change; energy transition; energy security; Paris agreement (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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