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

Mohan Kolhe

ISBN 10: 3639112970 / ISBN 13: 9783639112979

Published by VDM Verlag Dr. Müller, 2008

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - As wind is an intermittent generation resource andweather changes can cause large and rapid changes inoutput, system operators will need accurate androbust wind energy forecasting systems in the future.Rapid changes of wind generation relative to loadrequire rapid dispatching of generation andtransmission resources to balance generation versusload, regulate voltage and frequency, and maintainsystem performance within the limits established byNational Grid. Wind energy forecasts can help theenergy network operator to anticipate rapid changesof wind energy generation versus load and to make thedecisions. The study has been done for ANN and alsowith the combination of ANN and GA for short termwind power forecasting of wind power plants. Theperformance of these developed forecasting modelshave been tested and analyzed with wind power dataavailable from the operational records of wind powerplants. The results show that the combination of ANNand GA model does wind power output forecasting verywell except during the gust. This forecasting modelcan also be implemented in different time-scales,which will help wind energy trading in the openelectricity markets.

Seller Inventory # 9783639112979

Bibliographic Details

• Title: WIND ENERGY FORECASTING : BY USING ARTIFICIAL NEURAL NETWORK - GENETIC ALGORITHM
• Publisher: VDM Verlag Dr. Müller
• Publication Date: 2008
• Language: English
• ISBN 10: 3639112970
• ISBN 13: 9783639112979
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 22W x 15H x 0.3D centimeters 22 centimeters width by 15 centimeters height by 0.3 centimeters depth

About this title

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.

About the Author

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