Technology has been increasingly applied in search for excellence in water resource management. Tools such as demand-forecasting models provide information for utility companies to make operational, tactical and strategic decisions. Also, the performance of water distribution systems can be improved by anticipating consumption values. This work aimed to develop models to conduct monthly urban water demand forecasts by analyzing time series, and adjusting and testing forecast models by consumption category, which can be applied to any location. Open language R was used, with automatic procedures for selection, adjustment, model quality assessment and forecasts. The case study was conducted in the city of Joinville, with water consumption forecasts for the first semester of 2018. The results showed that the seasonal ARIMA method proved to be more adequate to predict water consumption in four out of five categories, with mean absolute percentage errors varying from 1.19 to 15.74%. In addition, a web application to conduct water consumption forecasts was developed.

为了寻求卓越的水资源管理，越来越多地采用了技术。需求预测模型之类的工具为公用事业公司提供信息，以使其制定运营，战术和战略决策。而且，可以通过预测消耗量来改善水分配系统的性能。这项工作旨在开发模型，通过分析时间序列以及按消费类别调整和测试预测模型来进行每月城市需水量预测，该模型可以应用于任何位置。使用开放语言R，并具有用于选择，调整，模型质量评估和预测的自动程序。该案例研究在Joinville市进行，预测了2018年上半年的用水量。结果表明，季节性ARIMA方法被证明更适合预测五类中的四类用水，平均绝对百分比误差在1.19％至15.74％之间。另外，开发了进行用水量预测的网络应用程序。