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From Hindcast to Forecast with Distributional Neural Networks in Water Demand Forecasting

Author(s): Gregor Johnen; Andre Niemann; Alexander Hutwalker; Christoph Donner

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Abstract: In recent years, the water supply-demand balance has become increasingly concerning to supply companies. Their goal is to control and manage resources efficiently. Visible demand patterns, resulting from the longterm effects of climate change, include demand peaks lasting multiple hours during the day or several days during prolonged dry periods, and numerous heat days throughout summer. Being able to make risk-based decisions, such as whether to fill up storage capacities or not, prior to events requires dependable short-term probabilistic forecasts.

DOI:

Year: 2024

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