Authors: Borja Ponte; David De la Fuente; Raúl Pino; Rafael Rosillo
Addresses: Polytechnic School of Engineering, University of Oviedo, Campus de Viesques, s/n, 33204, Gijón, Spain ' Polytechnic School of Engineering, University of Oviedo, Campus de Viesques, s/n, 33204, Gijón, Spain ' Polytechnic School of Engineering, University of Oviedo, Campus de Viesques, s/n, 33204, Gijón, Spain ' Faculty of Economics and Business, University of León, Campus de Vegazana, s/n, 24071, León, Spain
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.
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.
International Journal of Bio-Inspired Computation, 2015 Vol.7 No.3, pp.147 - 156
Available online: 25 May 2015Full-text access for editors Access for subscribers Purchase this article Comment on this article