Details

Autor(en) / Beteiligte

• Valença, Mêuser
• Ludermir, Teresa
• Valença, Anelle
• Jarvis, Ray
• Zhang, Shichao

Titel

River Flow Forecasting with Constructive Neural Network

Ist Teil von

• AI 2005: Advances in Artificial Intelligence, 2005, p.1031-1036

Ort / Verlag

Berlin, Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2005

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• In utilities using a mixture of hydroelectric and non-hydroelectric power, the economics of the hydroelectric plants depend upon the reservoir height and the inflow into the reservoir for several months into the future. Accurate forecasts of reservoir inflow allow the utility to feed proper amounts of fuel to individual plants, and to economically allocate the load between various non-hydroelectric plants. For this reasons, several companies in the Brazilian Electrical Sector use the linear time-series models such as PARMA (Periodic Auto regressive Moving Average) models. This paper provides for river flow prediction a numerical comparison between constructive neural networks and PARMA models. The results obtained in the evaluation of the performance of Neural Network were better than the results obtained with PARMA models.