A Critical Review on the Use of Artificial Intelligence in the Small Hydropower Industry

In recent years, Artificial Intelligence (AI) has begun to play a significant role across the renewable energy sector, yet its application within the Small Hydropower (SHP) sector remains underexplored compared to solar and wind. This review critically examines the state of AI integration in SHP, focusing on its potential to enhance forecasting accuracy, optimise operations, improve fault detection, and support sustainable environmental management. By synthesising evidence from recent advances across renewable energy sectors, the paper identifies both transferable methods, such as inflow forecasting adapted from solar irradiance prediction, and unique SHP challenges, including sedimentation, ecological flow management, and limited data availability. A comparative analysis demonstrates that while deep learning models such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) achieve high predictive performance, hybrid models that combine data-driven and physics-based approaches are particularly promising for data-scarce SHP environments. Economic considerations remain central, as AI integration often requires upfront investment in sensors and digital infrastructure, though long-term benefits in efficiency and reliability can outweigh costs. Furthermore, AI applications in SHP align with broader sustainability goals, contributing to the United Nations Sustainable Development Goals (SDGs) through improved energy access, resilient infrastructure, and climate action. The review highlights research gaps in collaborative learning, federated frameworks, and edge AI for rural deployments, underscoring the need for scalable and inclusive solutions. Ultimately, this paper positions AI as a critical enabler for the modernisation of SHP, offering a roadmap for advancing both technical innovation and sustainable development in the global energy transition.