WIND ENERGY FORECASTING: BY USING ARTIFICIAL NEURAL NETWORK - GENETIC ALGORITHM - Couverture souple

Kolhe, Dr Mohan

 
9783639112979: WIND ENERGY FORECASTING: BY USING ARTIFICIAL NEURAL NETWORK - GENETIC ALGORITHM
Couverture souple
ISBN 10 : 3639112970 ISBN 13 : 9783639112979
Editeur : VDM Verlag Dr. Müller, 2008

Synopsis

As wind is an intermittent generation resource and weather changes can cause large and rapid changes in output, system operators will need accurate and robust wind energy forecasting systems in the future. Rapid changes of wind generation relative to load require rapid dispatching of generation and transmission resources to balance generation versus load, regulate voltage and frequency, and maintain system performance within the limits established by National Grid. Wind energy forecasts can help the energy network operator to anticipate rapid changes of wind energy generation versus load and to make the decisions. The study has been done for ANN and also with the combination of ANN and GA for short term wind power forecasting of wind power plants. The performance of these developed forecasting models have been tested and analyzed with wind power data available from the operational records of wind power plants. The results show that the combination of ANN and GA model does wind power output forecasting very well except during the gust. This forecasting model can also be implemented in different time-scales, which will help wind energy trading in the open electricity markets.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

As wind is an intermittent generation resource and weather changes can cause large and rapid changes in output, system operators will need accurate and robust wind energy forecasting systems in the future. Rapid changes of wind generation relative to load require rapid dispatching of generation and transmission resources to balance generation versus load, regulate voltage and frequency, and maintain system performance within the limits established by National Grid. Wind energy forecasts can help the energy network operator to anticipate rapid changes of wind energy generation versus load and to make the decisions. The study has been done for ANN and also with the combination of ANN and GA for short term wind power forecasting of wind power plants. The performance of these developed forecasting models have been tested and analyzed with wind power data available from the operational records of wind power plants. The results show that the combination of ANN and GA model does wind power output forecasting very well except during the gust. This forecasting model can also be implemented in different time-scales, which will help wind energy trading in the open electricity markets.

Biographie de l'auteur

Dr Mohan Kolhe is with School of Engineering, Physics and Mathematics of the University of Dundee, UK. E-mail: M.L.Kolhe@dundee.ac.uk Tzu Chao Lin is with the Taiwan Power Company, Taiwan. E-mail: u772020@taipower.com.tw Dr Jussi Maunuksela is with the University of Jyvaskyla, Finland. E-mail: jussi.o.maunuksela@jyu.fi

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
VDM Verlag Dr. Müller
Date d'édition
2008
Langue
anglais
ISBN 10 
3639112970
ISBN 13 
9783639112979
Reliure
Broché
Nombre de pages
64
Coordonnées du fabricant
non disponible
Personne responsable
non disponible