Abstract
This study introduces a new Bayesian model for predicting water consumption in a medium-sized municipality in the State of São Paulo, Brazil. For the study, a stratified random sample of water consumption for consumers in different consumer categories (residential, industrial, public and commercial) is selected for 55 monthly consecutive measurements of water consumption and the proposed model is compared with some usual existing time series models (moving average models and ARIMA models) commonly used in forecasts. The Bayesian model for the consumption data assumes the presence of a random effect that captures the possible dependence between the monthly consumption for the different categories. A hierarchical Bayesian analysis is done using MCMC (Markov Chain Monte Carlo) methods to generate samples of the joint posterior distribution of interest. A detailed discussion of the results obtained is presented, showing the advantages and disadvantages of each model proposed in terms of feasibility for the municipality's water supply company. The results of this study can be generalized to water consumption data for any municipality.
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