Daily Urban Water Demand Forecasting - Comparative Study

Conference paper

DOI: 10.1007/978-3-319-34099-9_49

Part of the Communications in Computer and Information Science book series (CCIS, volume 613)
Cite this paper as:
Froelich W. (2016) Daily Urban Water Demand Forecasting - Comparative Study. In: Kozielski S., Mrozek D., Kasprowski P., Małysiak-Mrozek B., Kostrzewa D. (eds) Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery. BDAS 2015, BDAS 2016. Communications in Computer and Information Science, vol 613. Springer, Cham


There are many existing, general purpose models for the forecasting of time series. However, until now, only a small number of experimental studies exist whose goal is to select the forecasting model for a daily, urban water demand series. Moreover, most of the existing studies assume off-line access to data. In this study, we are confronted with the task to select the best forecasting model for the given water demand time series gathered from the water distribution system of Sosnowiec, Poland. In comparison to the existing works, we assume on-line availability of water demand data. Such assumption enables day-by-day retraining of the predictive model. To select the best individual approach, a systematic comparison of numerous state-of-the-art predictive models is presented. For the first time in this paper, we evaluate the approach of averaging forecasts with respect to the on-line available daily water demand time series. In addition, we analyze the influence of missing data, outliers, and external variables on the accuracy of forecasting. The results of experiments provide evidence that the average forecasts outperform all considered individual models, however, the selection of the models used for averaging is not trivial and must be carefully done. The source code of the preformed experiments is available upon request.


Forecasting water demand Comparative study Averaging forecasts 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Computer ScienceUniversity of SilesiaSosnowiecPoland

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