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The Development and Implementation of a Short Term Prediction Tool Using Artificial Neural Networks

Authors:
Aubai Alkhatib
Siegfried Heier
Melih Kurt

Keywords: Artificial Neural Networks; Wind Speed; Mean root square error; Forecasting.

Abstract:
Wind speed forecasting is an essential prerequisite for the planning, operation, and maintenance works associated with wind energy engineering. This paper attempts to forecast fluctuations based only on observed wind data using the data-driven artificial neural network approach. Wind fluctuations with varying lead times ranging from a half year to a full year are predicted at Al-Hijana, Syria with the pre-preparation for the available data. Two layers of feedforward back-propagation networks were used along with the conjugate gradient algorithm and other tested training functions. The results show that artificial neural network models perform extremely well as low values of errors resulting between the measured and predicted data are obtained. The present work contributes to previous work in the field of wind energy independent power producer market and may be of significant value to Syria, considering that the country is currently in the process of transitioning into a free energy market. It is likely that this modeling approach will become a useful tool to enable power producer companies to better forecast or supplement wind speed data.

Pages: 26 to 31

Copyright: Copyright (c) IARIA, 2011

Publication date: September 25, 2011

Published in: conference

ISSN: 2308-3735

ISBN: 978-1-61208-154-0

Location: Rome, Italy

Dates: from September 25, 2011 to September 30, 2011