Deep learning-based flow disaggregation for short-term hydropower plant operations
This addresses the need for intraday hydrological data to improve decision-making in hydropower operations, but it appears incremental as it applies existing deep learning methods to a specific domain gap.
The study tackled the problem of generating high-resolution hourly inflow data from daily data for hydropower plant operations using a deep learning-based time series disaggregation model, with preliminary results showing applicability but no concrete numbers reported.
High temporal resolution data plays a vital role in effective short-term hydropower plant operations. In the majority of the Norwegian hydropower system, inflow data is predominantly collected at daily resolutions through measurement installations. However, for enhanced precision in managerial decision-making within hydropower plants, hydrological data with intraday resolutions, such as hourly data, are often indispensable. To address this gap, time series disaggregation utilizing deep learning emerges as a promising tool. In this study, we propose a deep learning-based time series disaggregation model to derive hourly inflow data from daily inflow data for short-term hydropower plant operations. Our preliminary results demonstrate the applicability of our method, with scope for further improvements.