Akademik Veri Yönetim Sistemi
7th International Conference on Engineering Sciences, Ankara, Türkiye, 9 - 10 Şubat 2024
Wind
energy is essential for achieving a sustainable future since it offers a clean
and renewable power source. It plays a significant role in combating climate change,
decreasing reliance on limited fossil fuels, and promoting an environmentally
friendly and robust energy infrastructure. However, the intrinsic variability
of wind speeds and directions makes wind power unpredictable, which presents
issues for energy planners. Fluctuations in wind can result in intermittent
energy generation and significant disturbances in the stability of the power
grid. Thus, accurate short-term prediction of wind power is crucial for
optimizing energy grid operations and enabling efficient management of supply
and demand. In this study, six-year weather and power data for a wind power
facility located in İzmir/Türkiye was collected. 90% of the data was utilized
as training set, and 10% was retained as test set. Advanced artificial neural
network models (LSTM, NARX, and Elman) were utilized to estimate wind power
from the former 8 hours weather data. All models have performed sufficiently
well, while the LSTM model performed the best with a mean absolute error of
0.0495 and an R2 value of 0.9311. The proposed models here can be adapted to
any wind power facility around the globe, enabling successful optimization of
wind power’s contribution to the energy grid.