Publication
Multi-model prediction for demand forecast in water distribution networks
Journal Article (2018)
Authors
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López Farias, Rodrigo
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Puig Cayuela, Vicenç
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Rodríguez Rangel, Héctor
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Flores, Juan J.
Projects associated
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.
Categories
control theory.
Author keywords
prediction, multi-model, water demand, short-term prediction
Scientific reference
R. López, V. Puig, H. Rodríguez and J.J. Flores. Multi-model prediction for demand forecast in water distribution networks. Energies, 11(3): 660, 2018.
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