A Scalable AI-Driven Digital Twin Framework for Hydroelectric Power Plants

The growing U.S. energy demand, combined with the cost reduction and reliability enhancement needs of existing hydropower plants, necessitates smart modernization. Digital twin (DT) frameworks can enhance hydropower operations by leveraging advanced data-driven models for energy production optimization, degradation detection, anomaly prediction, and early-warning alarms. DTs can not only improve operational efficiency and asset longevity but also strengthen energy dominance in critical infrastructure by minimizing costly downtimes. Here, we present a new, scalable DT framework developed to support the aging fleet of energy-generating hydropower dams. This digital twin framework is built upon the application of a long short-term memory (LSTM) algorithm, enabling high-fidelity modeling and real-time monitoring of energy asset operations. By synthesizing preprocessed historical hydropower generating unit sensor and operational parameter data and using a model stacking approach, the DT achieves consistent accuracy in simulating multiple aspects of unit behavior, thereby enabling predictive insights. Following the description of the DT components, we demonstrate, using data collected at the Alder Dam in the Pacific Northwest, the framework application for hydropower unit characterization, showcase the DT’s routine (“online”) and simulation (“offline”) modes, available on our operational data portal, which hosts data from multiple hydropower facilities, and discuss future capabilities and expansions.