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
Link To Document :
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