Real-time water demand forecasting system through an agent-based architecture

Ponte Blanco, Borja, De la Fuente, David, Pino, Raúl and Rosillo, Rafael 2015. Real-time water demand forecasting system through an agent-based architecture. International Journal of Bio-Inspired Computation 7 (3) , pp. 147-156. 10.1504/IJBIC.2015.069559

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Abstract

Water policies have evolved enormously since the Rio Earth Summit (1992). These changes have led to the strategic importance of water demand management. The aim is to provide water where and when it is required using the fewest resources. A key variable in this process is the demand forecasting. It is not sufficient to have long term forecasts, as the current context requires the continuous availability of reliable hourly predictions. This paper incorporates artificial intelligence to the subject, through an agent-based system, whose basis are complex forecasting methods (Box-Jenkins, Holt-Winters, multi-layer perceptron networks and radial basis function networks). The prediction system also includes data mining, oriented to the pre and post processing of data and to the knowledge discovery, and other agents. Thereby, the system is capable of choosing at every moment the most appropriate forecast, reaching very low errors. It significantly improves the results of the different methods separately.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Uncontrolled Keywords:	Agent-based systems; Multi-agent systems; MAS; Artificial neural networks; ANNs; Box-Jenkins; Data mining; Demand forecasting; Holt-Winters; Hourly forecasting; Multi-layer perceptron; MLP; Radial basis function; RBF; Water demand management; WDM; Knowledge discovery; Water supply; Water management
Publisher:	Inderscience
ISSN:	1758-0366
Date of First Compliant Deposit:	7 February 2017
Date of Acceptance:	7 January 2015
Last Modified:	27 Nov 2024 11:15
URI:	https://orca.cardiff.ac.uk/id/eprint/98149

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