DocumentCode :
3737157
Title :
Short-term wind speed forecasting model based on ANN with statistical feature parameters
Author :
Christos S. Ioakimidis;Konstantinos N. Genikomsakis;Panagiotis I. Dallas;Sergio Lopez
Author_Institution :
ERA Chair ‘
fYear :
2015
Firstpage :
971
Lastpage :
976
Abstract :
The intermittent and unstable nature of wind raises significant challenges for the operation of wind power systems, either residential installations or utility-scale implementations, necessitating the development of reliable and accurate wind power forecasting techniques. Given that wind speed forecasting is typically considered the intermediate step for wind power forecasting, the present work proposes a novel short-term wind speed forecasting model based on an artificial neural network (ANN), with the key characteristic that statistical feature parameters of wind speed, wind direction and ambient temperature are employed in order to reduce the input vector and thus the complexity of the model. The results obtained indicate that the proposed model strikes a reasonable balance between accuracy and computational requirements for a forecasting time horizon of 24 hours, providing a light-weight solution that can be integrated as part of energy management systems for small scale applications.
Keywords :
"Wind speed","Forecasting","Predictive models","Wind forecasting","Computational modeling","Temperature distribution","Wind turbines"
Publisher :
ieee
Conference_Titel :
Industrial Electronics Society, IECON 2015 - 41st Annual Conference of the IEEE
Type :
conf
DOI :
10.1109/IECON.2015.7392225
Filename :
7392225
Link To Document :
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