Multi-Model Prediction for Demand Forecast in Water Distribution Networks

Rodrigo Lopez Farias, Vicenç Puig, Hector Rodriguez Rangel and Juan Flores
Additional contact information
Rodrigo Lopez Farias: CONACYT—Consorcio CENTROMET, Camino a Los Olvera 44, Los Olvera, Corregidora, Querétaro 76904, Mexico
Vicenç Puig: Institut de Robótica i Informática Industrial (CSIC-UPC), Carrer LLorens Artigas 4-6, Barcelona 08028, Spain
Hector Rodriguez Rangel: División de Estudios de Posgrado e Investigación, Instituto Tecnológico de Culiacán, Juan de Dios Bátiz 310 pte, Culiacán 80220, Mexico

Energies, 2018, vol. 11, issue 3, 1-21

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.

Keywords: prediction; multi-model; water demand; short-term prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/11/3/660/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/3/660/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:11:y:2018:i:3:p:660-:d:136427

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().