Wind power forecasting using advanced neural networks models
Journal Article
·
· IEEE Transactions on Energy Conversion
- Ecole des Mines de Paris, Sophia-Antipolis (France). Centre de`Energetique
- Technical Univ. of Crete (Greece). Dept. of Electronic and Computer Engineering
In this paper, an advanced model, based on recurrent high order neural networks, is developed for the prediction of the power output profile of a wind park. This model outperforms simple methods like persistence, as well as classical methods in the literature. The architecture of a forecasting model is optimized automatically by a new algorithm, that substitutes the usually applied trial-and-error method. Finally, the on-line implementation of the developed model into an advanced control system for the optimal operation and management of a real autonomous wind-diesel power system, is presented.
- OSTI ID:
- 438808
- Report Number(s):
- CONF-960725--
- Journal Information:
- IEEE Transactions on Energy Conversion, Journal Name: IEEE Transactions on Energy Conversion Journal Issue: 4 Vol. 11; ISSN 0885-8969; ISSN ITCNE4
- Country of Publication:
- United States
- Language:
- English
Similar Records
Online evolutionary neural architecture search for multivariate non-stationary time series forecasting
Wind speed and power forecasting based on spatial correlation models
Short-Term Forecasting Across a Network for the Autonomous Wind Farm
Journal Article
·
Thu Jun 15 00:00:00 EDT 2023
· Applied Soft Computing
·
OSTI ID:2422444
Wind speed and power forecasting based on spatial correlation models
Journal Article
·
Wed Sep 01 00:00:00 EDT 1999
· IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers)
·
OSTI ID:20001196
Short-Term Forecasting Across a Network for the Autonomous Wind Farm
Conference
·
Thu Aug 29 00:00:00 EDT 2019
·
OSTI ID:1569436