Name
Abstract | ||
|---|---|---|
Load forecasting is an important component for power system energy management system. Precise load forecasting helps the electric utility to make unit commitment decisions, reduce spinning reserve capacity and schedule device maintenance plan properly. This paper presents the development of a novel fuzzy wavelet neural network model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. In the proposed scheme, a wavelet neural network has replaced the classic TSK model in the consequent part, while subtractive clustering has been applied to the definition of fuzzy rules. The forecasting error statistical results, corresponding to the minimum and maximum load time-series, indicate that the proposed load forecasting model provides more accurate forecasts, compared to conventional neural networks models. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/FUZZY.2011.6007492 | Fuzzy Systems |
Keywords | Field | DocType |
fuzzy neural nets,load forecasting,power engineering computing,power system management,wavelet transforms,Greek Island of Crete,TSK model,error statistical results,fuzzy rules,fuzzy wavelet neural networks,maximum load time-series,minimum load time-series,power system energy management system,short-term electric load forecasting,spinning reserve capacity reduction,subtractive clustering,unit commitment decisions,Load forecasting,fuzzy wavelet networks neural networks,power systems | Data mining,Electrical load,Computer science,Fuzzy logic,Power system simulation,Electric power system,Energy management system,Artificial intelligence,Artificial neural network,Cluster analysis,Machine learning,Wavelet transform | Conference |
ISSN | ISBN | Citations |
1098-7584 E-ISBN : 978-1-4244-7316-8 | 978-1-4244-7316-8 | 1 |
PageRank | References | Authors |
0.39 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mahdi Amina | 1 | 32 | 2.75 |
| Vassilis S. Kodogiannis | 2 | 1 | 0.73 |