A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Multi-model prediction for demand forecast in water distribution networks
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 ; This work has been partially funded by the Spanish Ministry of Economy and Competitiveness (MINECO) and the European Union through FEDER program through the projects DEOCS (ref. DPI2016-76493-C3-3-R) and HARCRICS (ref. DPI2014-58104-R). ; Peer reviewed
Multi-model prediction for demand forecast in water distribution networks
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 ; This work has been partially funded by the Spanish Ministry of Economy and Competitiveness (MINECO) and the European Union through FEDER program through the projects DEOCS (ref. DPI2016-76493-C3-3-R) and HARCRICS (ref. DPI2014-58104-R). ; Peer reviewed
Multi-model prediction for demand forecast in water distribution networks
López Farías, Rodrigo (author) / Rodriguez Rangel, Hector (author) / Puig, Vicenç (author) / Flores, Juan J. (author) / Ministerio de Economía y Competitividad (España) / European Commission / López Farías, Rodrigo 0000-0003-2772-0051 / Rodriguez Rangel, Hector 0000-0003-4999-3472 / Flores, Juan J. 0000-0002-0379-7495
2018-03-15
Article (Journal)
Electronic Resource
English
DDC:
690
Water consumption in distribution networks. Short term demand forecast
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