Prediction of the outflow temperature of large-scale hydropower using theory-guided machine learning surrogate models of a high-fidelity hydrodynamics model

Abstract

Stratified water intake facilities are important pieces of engineering infrastructure that monitor the outflow temperature conditions of hydropower projects. The outflow temperature can significantly impact the downstream eco-environment and normal functioning of aquatic organisms in streams. However, due to the lack of scientific and effective management tools for stratified water intake and the complexity of the hydrodynamics associated with hydropower generation, both the research community and operation sectors are making great efforts to explore new tools and technology to better monitor, predict and control stratified intake facilities. In this study, surrogate models of the Environmental Fluid Dynamics Code (EFDC) model based on the theory-guided machine learning (TGML) paradigm are constructed. These models are applied to outflow temperature prediction for the Jinping-I Hydropower Plant in China. The results show that 1) the prediction precision of the high-fidelity hydrodynamic and water quality EFDC model is successfully emulated by a TGML model based on a long short-term memory (LSTM) algorithm; and 2) the TGML model accuracy, based on the mean absolute error (MAE) value obtained using the LSTM algorithm, can reach 0.228–0.269 °C, the prediction time is less than 2 s, the period of high-precision prediction is 6–13 days, and the model can guide the operation of stratified intake facilities in practice.