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Título: Real-Time Water Demand Forecasting System through an Agent-Based Architecture
Autores: Ponte, Borja
De la Fuente, David
Pino, Raúl
Rosillo, Rafael
Palabras Claves: Water Management
Demand Forecasting
Fecha Edición: 2015
Resumen: 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 wa-ter 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 arti-ficial intelligence to the subject, through an agent-based system, whose basis are complex fore-casting methods (Box-Jenkins, Holt-Winters, Multi-Layer Perceptron Networks and Radial Ba-sis 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
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