Reservoir Inflow Forecasting in Hydropower Industry: A Generative Flow-Based Approach

Forecasting the inflow of reservoirs plays an essential role in the hydropower industry. Existing studies are either limited to point estimates or inefficient in capturing higher-order dynamic correlations across data. It is nevertheless necessary to estimate data uncertainty in actual dam operation. This article presents a novel inflow prediction method that exploits generative flows to model complex multivariate hydrological time series. Our flow-to-flow method (F2F) augments the deterministic models with the normalizing flow-based generative networks to explicitly capture the multivariate correlations and approximate the predictive inflow distribution. Besides, F2F can quantify the prediction uncertainty to help interpret model behavior and predicted results while facilitating safety-critical decision-making on real-time reservoir operation. We conduct extensive experiments on real-world datasets collected from large-scale hydropower stations. The experimental results show that our method consistently outperforms existing methods while accounting for uncertain observations and providing tractable multistep ahead inflow forecasts.