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Multi-Model Prediction for Demand Forecast in Water Distribution Networks

Author

Listed:
  • Rodrigo Lopez Farias

    (CONACYT—Consorcio CENTROMET, Camino a Los Olvera 44, Los Olvera, Corregidora, Querétaro 76904, Mexico
    These authors contributed equally to this work.)

  • Vicenç Puig

    (Institut de Robótica i Informática Industrial (CSIC-UPC), Carrer LLorens Artigas 4-6, Barcelona 08028, Spain
    These authors contributed equally to this work.)

  • Hector Rodriguez Rangel

    (División de Estudios de Posgrado e Investigación, Instituto Tecnológico de Culiacán, Juan de Dios Bátiz 310 pte, Culiacán 80220, Mexico
    These authors contributed equally to this work.)

  • Juan J. Flores

    (División de Estudios de Posgrado de la Facultad de Ingeniería Eléctrica, Universidad Michoacana de San Nicolás de Hidalgo, Gral. Francisco J. Múgica S/N, Morelia 58040, Mexico
    These authors contributed equally to this work.)

Abstract

This paper presents a multi-model predictor called Qualitative Multi-Model Predictor Plus (QMMP+) for demand forecast in water distribution networks. QMMP+ is based on the decomposition of the quantitative and qualitative information of the time-series. The quantitative component (i.e., the daily consumption prediction) is forecasted and the pattern mode estimated using a Nearest Neighbor (NN) classifier and a Calendar. The patterns are updated via a simple Moving Average scheme. The NN classifier and the Calendar are executed simultaneously every period and the most suited model for prediction is selected using a probabilistic approach. The proposed solution for water demand forecast is compared against Radial Basis Function Artificial Neural Networks (RBF-ANN), the statistical Autoregressive Integrated Moving Average (ARIMA), and Double Seasonal Holt-Winters (DSHW) approaches, providing the best results when applied to real demand of the Barcelona Water Distribution Network. QMMP+ has demonstrated that the special modelling treatment of water consumption patterns improves the forecasting accuracy.

Suggested Citation

  • Rodrigo Lopez Farias & Vicenç Puig & Hector Rodriguez Rangel & Juan J. Flores, 2018. "Multi-Model Prediction for Demand Forecast in Water Distribution Networks," Energies, MDPI, vol. 11(3), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:660-:d:136427
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    References listed on IDEAS

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    1. J W Taylor, 2003. "Short-term electricity demand forecasting using double seasonal exponential smoothing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 799-805, August.
    2. Mahesh Kumar & Nitin Patel, 2010. "Using clustering to improve sales forecasts in retail merchandising," Annals of Operations Research, Springer, vol. 174(1), pages 33-46, February.
    3. Mauro De Marchis & Barbara Milici & Roberto Volpe & Antonio Messineo, 2016. "Energy Saving in Water Distribution Network through Pump as Turbine Generators: Economic and Environmental Analysis," Energies, MDPI, vol. 9(11), pages 1-15, October.
    4. Linas Gelažanskas & Kelum A. A. Gamage, 2015. "Forecasting Hot Water Consumption in Residential Houses," Energies, MDPI, vol. 8(11), pages 1-16, November.
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    2. Kang-Min Koo & Kuk-Heon Han & Kyung-Soo Jun & Gyumin Lee & Jung-Sik Kim & Kyung-Taek Yum, 2021. "Performance Assessment for Short-Term Water Demand Forecasting Models on Distinctive Water Uses in Korea," Sustainability, MDPI, vol. 13(11), pages 1-18, May.
    3. Chen, Ying & Koch, Thorsten & Zakiyeva, Nazgul & Zhu, Bangzhu, 2020. "Modeling and forecasting the dynamics of the natural gas transmission network in Germany with the demand and supply balance constraint," Applied Energy, Elsevier, vol. 278(C).
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