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.
Highlights
Water is one of the most important natural resources to sustain life and to guarantee people’s quality of life
The main contribution of this paper is the introduction of the probabilistic selection of qualitative model predictors and estimators, included in the Multi-model predictor architecture called Qualitative Multi-Model Predictor method (QMMP)+
Seasonal Autoregressive Integrated Moving Average (ARIMA) is suitable for predicting the daily consumption prediction, and Nearest Neighbor Mode Estimation (NNME)
Summary
Water is one of the most important natural resources to sustain life and to guarantee people’s quality of life. Complex drinking water distribution network systems provide water supply from the water reservoirs to the population. The main reservoirs are conformed by lakes, rivers, and sea water, among other sources. Water transportation implies energy cost and water availability, subject to population growth and water shortage. Important objectives in water delivery through drinking water networks are the supply of the water demanded by the consumers, the reduction of operational costs related to production and transportation [1], and the maximization of the water delivery service satisfaction, among others
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