Urban Water Demand Forecasting Using DeepAR-Models as Part of the Battle of Water Demand Forecasting (BWDF)
The accurate and reliable short-term forecasting of urban water demand plays a crucial role in enabling drinking water utilities to operate sustainably and secure water supplies in the future. Here, we apply state-of-the-art DeepAR models to predict urban water demand in ten district metered areas (...
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| Language: | English |
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MDPI AG
2024-09-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/69/1/25 |
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| author | Andreas Wunsch Christian Kühnert Steffen Wallner Mathias Ziebarth |
| author_facet | Andreas Wunsch Christian Kühnert Steffen Wallner Mathias Ziebarth |
| author_sort | Andreas Wunsch |
| collection | DOAJ |
| description | The accurate and reliable short-term forecasting of urban water demand plays a crucial role in enabling drinking water utilities to operate sustainably and secure water supplies in the future. Here, we apply state-of-the-art DeepAR models to predict urban water demand in ten district metered areas (DMAs) in a water distribution system in northeastern Italy. DeepAR models are based on long short-term memory networks and can directly provide probabilistic results. For this contribution, we leverage past flow data, current and future weather data, and engineered weather and date features as input to predict flow data one week ahead. A local model for each DMA is prepared and applied after hyperparameter optimization. |
| format | Article |
| id | doaj-art-8ed682ebe3784dda97a85e07e2e16077 |
| institution | OA Journals |
| issn | 2673-4591 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-8ed682ebe3784dda97a85e07e2e160772025-08-20T02:11:05ZengMDPI AGEngineering Proceedings2673-45912024-09-016912510.3390/engproc2024069025Urban Water Demand Forecasting Using DeepAR-Models as Part of the Battle of Water Demand Forecasting (BWDF)Andreas Wunsch0Christian Kühnert1Steffen Wallner2Mathias Ziebarth3Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Fraunhoferstrasse 1, 76131 Karlsruhe, GermanyFraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Fraunhoferstrasse 1, 76131 Karlsruhe, GermanyFraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Fraunhoferstrasse 1, 76131 Karlsruhe, GermanyFraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Fraunhoferstrasse 1, 76131 Karlsruhe, GermanyThe accurate and reliable short-term forecasting of urban water demand plays a crucial role in enabling drinking water utilities to operate sustainably and secure water supplies in the future. Here, we apply state-of-the-art DeepAR models to predict urban water demand in ten district metered areas (DMAs) in a water distribution system in northeastern Italy. DeepAR models are based on long short-term memory networks and can directly provide probabilistic results. For this contribution, we leverage past flow data, current and future weather data, and engineered weather and date features as input to predict flow data one week ahead. A local model for each DMA is prepared and applied after hyperparameter optimization.https://www.mdpi.com/2673-4591/69/1/25water demand forecastingmachine learningdeep learningDeepARLSTM |
| spellingShingle | Andreas Wunsch Christian Kühnert Steffen Wallner Mathias Ziebarth Urban Water Demand Forecasting Using DeepAR-Models as Part of the Battle of Water Demand Forecasting (BWDF) Engineering Proceedings water demand forecasting machine learning deep learning DeepAR LSTM |
| title | Urban Water Demand Forecasting Using DeepAR-Models as Part of the Battle of Water Demand Forecasting (BWDF) |
| title_full | Urban Water Demand Forecasting Using DeepAR-Models as Part of the Battle of Water Demand Forecasting (BWDF) |
| title_fullStr | Urban Water Demand Forecasting Using DeepAR-Models as Part of the Battle of Water Demand Forecasting (BWDF) |
| title_full_unstemmed | Urban Water Demand Forecasting Using DeepAR-Models as Part of the Battle of Water Demand Forecasting (BWDF) |
| title_short | Urban Water Demand Forecasting Using DeepAR-Models as Part of the Battle of Water Demand Forecasting (BWDF) |
| title_sort | urban water demand forecasting using deepar models as part of the battle of water demand forecasting bwdf |
| topic | water demand forecasting machine learning deep learning DeepAR LSTM |
| url | https://www.mdpi.com/2673-4591/69/1/25 |
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