Reliable short-term hydropower forecasting is essential for dispatch planning and early electricity procurement in snowmelt-influenced power systems. This study develops a leak-free operational forecasting framework using quality-controlled hourly generation and hydro-meteorological records from eight hydropower plants in Kazakhstan. Two tasks are addressed: deterministic multi-step forecasting for D+1–D+7 and uncertainty-aware envelope forecasting for D+8–D+14 using MIN and Q90 targets. The benchmark uses Persistence as the primary baseline, against which RIDGE, SARIMAX, Random Forest, HistGradientBoosting, MLP, and LSTM are compared using Nash–Sutcliffe efficiency (NSE), root mean squared error (RMSE), and mean absolute error (MAE). For D+1–D+7, the results reveal strong cross-station heterogeneity and the expected decline in skill with increasing lead time. In the aggregated comparison, SARIMAX achieves the highest mean NSE at D+1 (0.903), while RIDGE becomes strongest by D+7 (0.625), both outperforming Persistence (0.534 at D+7). At the station level, SARIMAX performs best for Kapch, Kask, Moin, Bukh, and Ustk, RIDGE is best for Shar and Lenin, and LSTM is best for Shulb. The strongest stations, Kapch and Kask, reach mean NSE values of 0.941 and 0.933, respectively, whereas Ustk and Bukh remain the most difficult cases. A central methodological contribution is a flood-sensitive switched hybrid strategy for Ust-Kamenogorsk based on an observed-generation high-flow window selected by a regime-score procedure. This strategy improves robustness at medium lead times: for SARIMAX, NSE increases from 0.587 to 0.739 at D+2 and from 0.161 to 0.559 at D+7, while for RIDGE, NSE increases from 0.549 to 0.701 at D+2 and from 0.109 to 0.435 at D+7, together with substantial RMSE and MAE reductions. For D+8–D+14, envelope forecasting remains informative, but model ranking becomes target-dependent: SARIMAX and RIDGE provide the strongest mean performance for MIN (0.664 and 0.658), whereas LSTM and RIDGE are strongest for Q90 (0.746 and 0.743). Overall, the results show that hydropower forecasting in Kazakhstan is best approached as a station-wise, regime-aware, and horizon-specific problem.
Rakhimzhanova et al. (Sat,) studied this question.