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A Real-Time Data Analysis Platform for Short-Term Water Consumption Forecasting with Machine Learning

Author

Listed:
  • Aida Boudhaouia

    (IRIMAS Laboratory, University of Haute Alsace, 61 rue Albert Camus, 68093 Mulhouse, France)

  • Patrice Wira

    (IRIMAS Laboratory, University of Haute Alsace, 61 rue Albert Camus, 68093 Mulhouse, France)

Abstract

This article presents a real-time data analysis platform to forecast water consumption with Machine-Learning (ML) techniques. The strategy fully relies on a web-oriented architecture to ensure better management and optimized monitoring of water consumption. This monitoring is carried out through a communicating system for collecting data in the form of unevenly spaced time series. The platform is completed by learning capabilities to analyze and forecast water consumption. The analysis consists of checking the data integrity and inconsistency, in looking for missing data, and in detecting abnormal consumption. Forecasting is based on the Long Short-Term Memory (LSTM) and the Back-Propagation Neural Network (BPNN). After evaluation, results show that the ML approaches can predict water consumption without having prior knowledge about the data and the users. The LSTM approach, by being able to grab the long-term dependencies between time steps of water consumption, allows the prediction of the amount of consumed water in the next hour with an error of some liters and the instants of the 5 next consumed liters in some milliseconds.

Suggested Citation

  • Aida Boudhaouia & Patrice Wira, 2021. "A Real-Time Data Analysis Platform for Short-Term Water Consumption Forecasting with Machine Learning," Forecasting, MDPI, vol. 3(4), pages 1-13, September.
    • Handle: RePEc:gam:jforec:v:3:y:2021:i:4:p:42-694:d:643473
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    References listed on IDEAS

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    1. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    2. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    3. Fotios Petropoulos & Evangelos Spiliotis, 2021. "The Wisdom of the Data: Getting the Most Out of Univariate Time Series Forecasting," Forecasting, MDPI, vol. 3(3), pages 1-20, June.
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    Cited by:

    1. Sonia Leva, 2022. "Editorial for Special Issue: “Feature Papers of Forecasting 2021”," Forecasting, MDPI, vol. 4(1), pages 1-3, March.

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