Models for forecasting water demand using time series analysis: a case study in Southern Brazil

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...

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Bibliographic Details
Published in:Journal of water, sanitation, and hygiene for development sanitation, and hygiene for development, 2021-03, Vol.11 (2), p.231-240
Main Authors: Ristow, Danielle C. M., Henning, Elisa, Kalbusch, Andreza, Petersen, Cesar E.
Format: Article
Language:English
Subjects:
Applications programs
arima
Case studies
Demand analysis
exponential smoothing
Forecasting
forecasting water demand
Mathematical models
Methods
Normal distribution
Quality assessment
Quality control
Resource management
Seasonal variations
Software
Time series
Trends
Water consumption
Water demand
Water distribution
Water distribution systems
Water engineering
Water resources
Water resources management
Water supply
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Summary: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.
ISSN:2043-9083
2408-9362
DOI:10.2166/washdev.2021.208