Name
Abstract | ||
|---|---|---|
The prediction of seasonal streamflow series is very important in countries where power generation is predominantly done by hydroelectric plants. Echo state networks can be safely regarded as promising tools in forecasting because they are recurrent networks that have a simple and efficient training process based on linear regression. Recently, Boccato et al. proposed a new architecture in which the output layer is built using a principal component analysis and a Volterra filter. This work performs a comparative investigation between the performances of different ESNs in the context of the forecasting of seasonal streamflow series associated with Brazilian hydroelectric plants. Two possible reservoir design approaches were tested with the classical and the Volterra-based output layer structures, and a multilayer perceptron was also included to establish bases for comparison. The obtained results show the relevance of these networks and also contribute to a better understanding of their applicability to forecasting problems. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-32639-4_28 | IDEAL |
Keywords | Field | DocType |
comparative investigation,seasonal streamflow series forecasting,echo state network,better understanding,volterra-based output layer structure,different esns,output layer,brazilian hydroelectric plant,volterra filter,hydroelectric plant,seasonal streamflow series,pca,forecasting | Streamflow,Computer science,Volterra filters,Multilayer perceptron,Artificial intelligence,Hydroelectricity,Principal component analysis,Electricity generation,Machine learning,Linear regression | Conference |
Citations | PageRank | References |
2 | 0.36 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hugo Siqueira | 1 | 14 | 3.60 |
| Levy Boccato | 2 | 45 | 5.78 |
| Romis Attux | 3 | 95 | 22.67 |
| Christiano Lyra Filho | 4 | 2 | 1.38 |