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Article

Application of LSTM Networks for Water Demand Prediction in Optimal Pump Control

1
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Fraunhofer Center for Machine Learning, Fraunhoferstraße 1, 76149 Karlsruhe, Germany
2
Fraunhofer Institute for Industrial Mathematics ITWM, Fraunhofer Center for Machine Learning, Fraunhofer-Platz 1, 67663 Kaiserslautern, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Joong Hoon Kim and Donghwi Jung
Water 2021, 13(5), 644; https://doi.org/10.3390/w13050644
Received: 29 January 2021 / Accepted: 23 February 2021 / Published: 28 February 2021
(This article belongs to the Special Issue Machine Learning for Hydro-Systems)
Every morning, water suppliers need to define their pump schedules for the next 24 h for drinking water production. Plans must be designed in such a way that drinking water is always available and the amount of unused drinking water pumped into the network is reduced. Therefore, operators must accurately estimate the next day’s water consumption profile. In real-life applications with standard consumption profiles, some expert system or vector autoregressive models are used. Still, in recent years, significant improvements for time series prediction have been achieved through special deep learning algorithms called long short-term memory (LSTM) networks. This paper investigates the applicability of LSTM models for water demand prediction and optimal pump control and compares LSTMs against other methods currently used by water suppliers. It is shown that LSTMs outperform other methods since they can easily integrate additional information like the day of the week or national holidays. Furthermore, the online- and transfer-learning capabilities of the LSTMs are investigated. It is shown that LSTMs only need a couple of days of training data to achieve reasonable results. As the focus of the paper is on the real-world application of LSTMs, data from two different water distribution plants are used for benchmarking. Finally, it is shown that the LSTMs significantly outperform the system currently in operation. View Full-Text
Keywords: decision support systems; long short-term memory networks; transfer and online learning; optimal pump control; time-series prediction; water consumption profiles decision support systems; long short-term memory networks; transfer and online learning; optimal pump control; time-series prediction; water consumption profiles
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MDPI and ACS Style

Kühnert, C.; Gonuguntla, N.M.; Krieg, H.; Nowak, D.; Thomas, J.A. Application of LSTM Networks for Water Demand Prediction in Optimal Pump Control. Water 2021, 13, 644. https://doi.org/10.3390/w13050644

AMA Style

Kühnert C, Gonuguntla NM, Krieg H, Nowak D, Thomas JA. Application of LSTM Networks for Water Demand Prediction in Optimal Pump Control. Water. 2021; 13(5):644. https://doi.org/10.3390/w13050644

Chicago/Turabian Style

Kühnert, Christian, Naga Mamatha Gonuguntla, Helene Krieg, Dimitri Nowak, and Jorge A. Thomas. 2021. "Application of LSTM Networks for Water Demand Prediction in Optimal Pump Control" Water 13, no. 5: 644. https://doi.org/10.3390/w13050644

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