IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i3p660-d136427.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/3/660/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/3/660/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Linas Gelažanskas & Kelum A. A. Gamage, 2015. "Forecasting Hot Water Consumption in Residential Houses," Energies, MDPI, vol. 8(11), pages 1-16, November.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jindamas Sutthichaimethee & Kuskana Kubaha, 2018. "Forecasting Energy-Related Carbon Dioxide Emissions in Thailand’s Construction Sector by Enriching the LS-ARIMAXi-ECM Model," Sustainability, MDPI, vol. 10(10), pages 1-19, October.
    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).
    4. Pauline Macharia & Nzula Kitaka & Paul Yillia & Norbert Kreuzinger, 2021. "Assessing Future Water Demand and Associated Energy Input with Plausible Scenarios for Water Service Providers (WSPs) in Sub-Saharan Africa," Energies, MDPI, vol. 14(8), pages 1-22, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hu, Junjie & López Cabrera, Brenda & Melzer, Awdesch, 2021. "Advanced statistical learning on short term load process forecasting," IRTG 1792 Discussion Papers 2021-020, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    2. Reham Alhindawi & Yousef Abu Nahleh & Arun Kumar & Nirajan Shiwakoti, 2020. "Projection of Greenhouse Gas Emissions for the Road Transport Sector Based on Multivariate Regression and the Double Exponential Smoothing Model," Sustainability, MDPI, vol. 12(21), pages 1-18, November.
    3. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    4. Barrow, Devon K., 2016. "Forecasting intraday call arrivals using the seasonal moving average method," Journal of Business Research, Elsevier, vol. 69(12), pages 6088-6096.
    5. Cancelo, José Ramón & Espasa, Antoni & Grafe, Rosmarie, 2008. "Forecasting the electricity load from one day to one week ahead for the Spanish system operator," International Journal of Forecasting, Elsevier, vol. 24(4), pages 588-602.
    6. Pesantez, Jorge E. & Li, Binbin & Lee, Christopher & Zhao, Zhizhen & Butala, Mark & Stillwell, Ashlynn S., 2023. "A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment," Energy, Elsevier, vol. 283(C).
    7. Chabouni, Naima & Belarbi, Yacine & Benhassine, Wassim, 2020. "Electricity load dynamics, temperature and seasonality Nexus in Algeria," Energy, Elsevier, vol. 200(C).
    8. Myoungsoo Kim & Wonik Choi & Youngjun Jeon & Ling Liu, 2019. "A Hybrid Neural Network Model for Power Demand Forecasting," Energies, MDPI, vol. 12(5), pages 1-17, March.
    9. Amaral, Luiz Felipe & Souza, Reinaldo Castro & Stevenson, Maxwell, 2008. "A smooth transition periodic autoregressive (STPAR) model for short-term load forecasting," International Journal of Forecasting, Elsevier, vol. 24(4), pages 603-615.
    10. Sinagra, Marco & Aricò, Costanza & Tucciarelli, Tullio & Morreale, Gabriele, 2020. "Experimental and numerical analysis of a backpressure Banki inline turbine for pressure regulation and energy production," Renewable Energy, Elsevier, vol. 149(C), pages 980-986.
    11. Theresa Maria Rausch & Tobias Albrecht & Daniel Baier, 2022. "Beyond the beaten paths of forecasting call center arrivals: on the use of dynamic harmonic regression with predictor variables," Journal of Business Economics, Springer, vol. 92(4), pages 675-706, May.
    12. Karamaziotis, Panagiotis I. & Raptis, Achilleas & Nikolopoulos, Konstantinos & Litsiou, Konstantia & Assimakopoulos, Vassilis, 2020. "An empirical investigation of water consumption forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(2), pages 588-606.
    13. Changrui Deng & Xiaoyuan Zhang & Yanmei Huang & Yukun Bao, 2021. "Equipping Seasonal Exponential Smoothing Models with Particle Swarm Optimization Algorithm for Electricity Consumption Forecasting," Energies, MDPI, vol. 14(13), pages 1-14, July.
    14. Linas Gelažanskas & Kelum A. A. Gamage, 2016. "Distributed Energy Storage Using Residential Hot Water Heaters," Energies, MDPI, vol. 9(3), pages 1-13, February.
    15. J D Bermúdez & J V Segura & E Vercher, 2006. "Improving demand forecasting accuracy using nonlinear programming software," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(1), pages 94-100, January.
    16. Seunghyoung Ryu & Jaekoo Noh & Hongseok Kim, 2016. "Deep Neural Network Based Demand Side Short Term Load Forecasting," Energies, MDPI, vol. 10(1), pages 1-20, December.
    17. Venturini, Mauro & Manservigi, Lucrezia & Alvisi, Stefano & Simani, Silvio, 2018. "Development of a physics-based model to predict the performance of pumps as turbines," Applied Energy, Elsevier, vol. 231(C), pages 343-354.
    18. Clements, A.E. & Hurn, A.S. & Li, Z., 2016. "Forecasting day-ahead electricity load using a multiple equation time series approach," European Journal of Operational Research, Elsevier, vol. 251(2), pages 522-530.
    19. Jahanpour, Ehsan & Ko, Hoo Sang & Nof, Shimon Y., 2016. "Collaboration protocols for sustainable wind energy distribution networks," International Journal of Production Economics, Elsevier, vol. 182(C), pages 496-507.
    20. Mauro Venturini & Stefano Alvisi & Silvio Simani & Lucrezia Manservigi, 2018. "Comparison of Different Approaches to Predict the Performance of Pumps As Turbines (PATs)," Energies, MDPI, vol. 11(4), pages 1-17, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:660-:d:136427. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.