Previsão de demanda para sistema de abastecimento de água / Water demand prediction for water distribution system

AUTOR(ES)
DATA DE PUBLICAÇÃO

2010

RESUMO

The present work focuses the problem of water demand forecasting for real time operation of WSS. The study was conducted using hourly consumption data from water distribution system from the cities of São Carlos, Araraquara, SP, to identify the model that fits better. It were studied the artificial neural network Multilayer Perceptron (ANN MLP), the Dynamic Neural Network (DAN2) and two hybrid ANN. The hybrid ANN is an association of the water demand prevision by series of Fourier with the ANN MLP and DAN2, which were called respectively ANN-H and DAN2-H. The inputs provided to the forecasting models were chosen based on literature review and correlation analysis, considering consumption data and meteorological variables, such as temperature, air relative humidity and rain occurrence. The best forecasting models were based on DAN2, which showed easy handling compared to other neural network with multiple layers, because it dispenses the trial and error procedure to find the best architecture for a given data. The best forecasting model for the next hour produced an absolute medium error of 2.25 L/s (DAN2-H) for a subsector from Araraquara, representing about 8% of the average consumption, and 2.30 L/s (DAN2) for a sector from São Carlos, which correspond to 4% of its average consumption.

ASSUNTO(S)

abastecimento de água forecasting supply system previsão de demanda neural network rede neural

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