Probabilistic Multi-Step-Ahead Short-Term Water Demand Forecasting with Lasso
Publication: Journal of Water Resources Planning and Management
Volume 146, Issue 10
Abstract
Water demand is a highly important variable for operational control and decision making. Therefore, the development of accurate forecasts is a valuable field of research to further improve the efficiency of water utilities. Focusing on probabilistic multi-step-ahead forecasting, a time series model is introduced to capture typical autoregressive, calendar, and seasonal effects; to account for time-varying variance; and to quantify the uncertainty and path-dependency of the water demand process. To deal with the high complexity of the water demand process, a high-dimensional feature space is applied, which is efficiently tuned by an automatic shrinkage and selection operator (lasso). It allows to obtain an accurate, simple interpretable and fast computable forecasting model, which is well suited for real-time applications. The complete probabilistic forecasting framework allows not only for simulating the mean and the marginal properties, but also the correlation structure between hours within the forecasting horizon. For practitioners, complete probabilistic multi-step-ahead forecasts are of considerable relevance as they provide additional information about the expected aggregated or cumulative water demand, so that a statement can be made about the probability with which a water storage capacity can guarantee the supply over a certain period of time. This information allows to better control storage capacities and to better ensure the smooth operation of pumps. To appropriately evaluate the forecasting performance of the considered models, the energy score (ES) as a strictly proper multidimensional evaluation criterion, is introduced. The methodology is applied to the hourly water demand data of a German water supplier.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions (e.g., anonymized data).
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Published In
Journal of Water Resources Planning and Management
Volume 146 • Issue 10 • October 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Jul 2, 2019
Accepted: Mar 16, 2020
Published online: Jul 16, 2020
Published in print: Oct 1, 2020
Discussion open until: Dec 16, 2020
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