Forecasting Daily Water Demand Using Fuzzy Cognitive Maps

  • Jose L. Salmeron
  • Wojciech Froelich
  • Elpiniki I. Papageorgiou
Conference paper

DOI: 10.1007/978-3-319-28725-6_24

Part of the Contributions to Statistics book series (CONTRIB.STAT.)
Cite this paper as:
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

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

Forecasting water demand Fuzzy cognitive maps 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jose L. Salmeron
    • 1
  • Wojciech Froelich
    • 2
  • Elpiniki I. Papageorgiou
    • 3
  1. 1.University Pablo de OlavideSevilleSpain
  2. 2.The University of SilesiaSosnowiecPoland
  3. 3.Computer Engineering DepartmentTechnological Educational Institute of Central GreeceLamiaGreece

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