• DocumentCode
    1797753
  • Title

    Computational Intelligence in Smart water and gas grids: An up-to-date overview

  • Author

    Fagiani, Marco ; Squartini, Stefano ; Gabrielli, Leonardo ; Pizzichini, Mirco ; Spinsante, Susanna

  • Author_Institution
    Dept. of Inf. Eng., Univ. Politec. delle Marche, Ancona, Italy
  • fYear
    2014
  • fDate
    6-11 July 2014
  • Firstpage
    921
  • Lastpage
    926
  • Abstract
    Computational Intelligence plays a relevant role in several Smart Grid applications, and there is a florid literature in this regard. However, most of the efforts have been oriented to the electrical energy field, for which many contributions have appeared so far, also facilitated by the availability of suitable databases to use for system training and testing. Different is the case for the water and gas scenarios: this work is thus oriented to present the state-of-the-art techniques for these grids, from 2009 to date. In particular, the focus is on load forecasting and leakage detection applications, that are the most addressed in the literature and present the biggest interest from a commercial point of view as well: the main characteristics and registered performance for all the reviewed approaches are reported. Along this direction, an extensive search of used databases has been performed and thus made available to the research community.
  • Keywords
    database management systems; leak detection; load forecasting; power engineering computing; smart power grids; water supply; computational intelligence; databases; electrical energy field; leakage detection applications; load forecasting; smart gas grids; smart grid applications; smart water; system testing; system training; Databases; Forecasting; Natural gas; Neural networks; Predictive models; Pressure measurement; Sensors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), 2014 International Joint Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4799-6627-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.2014.6889603
  • Filename
    6889603