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.

翻译：高时间分辨率数据在短期水电站高效运行中起着关键作用。在挪威大部分水电系统中，入流数据主要通过测量设施以日分辨率采集。然而，为提升水电站管理决策的精准度，往往需要日内分辨率（如小时级数据）的水文数据。为解决这一局限，利用深度学习进行时间序列解聚成为一种有前景的手段。本研究提出一种基于深度学习的时间序列解聚模型，从日入流数据中推导小时级入流数据，以支持短期水电站运行。初步结果验证了该方法的适用性，并为进一步改进提供了空间。