Jens Kley-Holsteg, Florian Ziel
arXiv 9 May 2020 · Statistics — Applications · 7 citations (OpenAlex)
arXiv:2005.04522 · PDF · OpenAlex · Extracted main text
Water demand is a highly important variable for operational control and decision making. Hence, 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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Alvisi, S. and Franchini, M (2017) Assessment of predictive uncertainty within the framework of water demand forecasting using the model conditional processor (mcp) | 1.000 | 7 | 3 | 100% |
| 2 | Pacchin, E., Gagliardi, F., Alvisi, S., and Franchini, M (2019) A comparison of short-term water demand forecasting models | 1.000 | 6 | 3 | 100% |
| 3 | Arandia, E., Ba, A., Eck, B., and McKenna, S (2016) Tailoring seasonal time series models to forecast short-term water demand | 1.000 | 5 | 3 | 100% |
| 4 | Hutton, C. J. and Kapelan, Z (2015) A probabilistic methodology for quantifying, diagnosing and reducing model structural and predictive errors in short term water… | 0.928 | 4 | 3 | 100% |
| 5 | Herrera, M., Torgo, L., Izquierdo, J., and Pérez-García, R (2010) Predictive models for forecasting hourly urban water demand | 0.874 | 6 | 2 | 100% |
| 6 | Brentan, B. M., Luvizotto Jr., E., Herrera, M., Izquierdo, J., and P… (2017) Hybrid regression model for near real-time urban water demand forecasting | 0.843 | 3 | 3 | 100% |
| 7 | Chen, J. and Boccelli, D. L (2018) Forecasting hourly water demands with seasonal autoregressive models for real-time application | 0.737 | 3 | 2 | 100% |
| 8 | Adamowski, J. and Karapataki, C (2010) Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: Evaluation of diff… | 0.644 | 2 | 2 | 100% |
| 9 | Adamowski, J., Fung Chan, H., Prasher, S. O., Ozga-Zielinski, B., an… (2012) Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and… | 0.644 | 2 | 2 | 100% |
| 10 | Alvisi, S., Franchini, M., and Marinelli, A (2007) A short-term, pattern-based model for water-demand forecasting | 0.644 | 2 | 2 | 100% |
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