Abstract
In this chapter, we describe the design of a multi-regressive forecasting model based on fuzzy cognitive maps (FCMs). Growing window approach and 1-day ahead forecasting are assumed. The proposed model is retrained every day as more data become available. To improve forecasting accuracy, mean daily temperature and precipitation are applied as additional explanatory variables. The designed model is trained and tested using data gathered from a water distribution system. Comparative experiments provide evidence for the superiority of the proposed approach over the selected state-of-the-art competitive methods.
Keywords
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Acknowledgements
The work was supported by ISS-EWATUS project which has received funding from the European Union’s Seventh Framework Programme for research, technological development, and demonstration under grant agreement no. 619228. The authors would like to thank the water distribution company in Sosnowiec (Poland) for gathering water demand data and the personal of the weather station of the University of Silesia for collecting and preparing meteorological data.
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Salmeron, J.L., Froelich, W., Papageorgiou, E.I. (2016). Forecasting Daily Water Demand Using Fuzzy Cognitive Maps. In: Rojas, I., Pomares, H. (eds) Time Series Analysis and Forecasting. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-28725-6_24
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