Akademik Veri Yönetim Sistemi
5 th International Artemis Congress on Life, Engineering, and Applied Sciences, İzmir, Türkiye, 1 - 03 Ekim 2023, cilt.1, ss.1-2
Wind is one of the important effects used in the field of meteorology. It is very important to know wind
speed because wind uses in clean energy generation, agricultural calculations, calculations of load acting
on structures and having a low carbon footprint, being sustainable and provides energy independence.
General Directorate of Renewable Energy foreseen will be increased the share of renewable energy
resources in total electricity generation to 30% in 2023 in the 2013-2023 Period Turkey National
Renewable Energy Action Plan. The share of renewable energy generation in total energy generation
in 2021 is 19.1% according to the Turkish Statistical Institute. The rate of wind power in licensed total
electrical energy generation was stated as 9.71% in the 2021 report of the Energy Market Regulatory
Authority. Accurate wind speed forecasting is required in order for renewable energy sources to reach
the projected share. According to the Turkish Statistical Institute, based on Turkey’s economic gross
domestic product, the ratio of the agriculture and forest sector to the total of sectors is approximately
6% for 2021. This rate is approximately 16% for Sivas province. The fact that Sivas province’s share
of gross domestic product in the agriculture and forestry sector is above Turkey’s average shows the
importance of evapotranspiration and the related wind speed value in Sivas province. For this reason, in
the study, a wind speed forecasting study was carried out with the Long Short Term Memory machine
learning method, using the wind speed values belong the stations in Sivas province and its surroundings.
Wind speed values of Sivas Center, Suşehri, Kangal, Gemerek and Divriği stations located in Sivas
province were obtained from the General Directorate of Meteorology. The data set consists of 432
data between the years 1986-2021. 75% of the data was used in training the model and 25% was used
in the testing phase. Mean absolute error (MAE), root mean square error (RMSE) and coefficient of
determination (R2
) performance criteria were used to determine the model performance. MAE values
were calculated for each station, respectively 0.1348; 0.6848; 0.6075; 0.2266; 0.2803; RMSE values
were calculated 0.1400; 0.7539; 0.6377; 0.2618; 0.3238; R2 values were calculated 0.9917; 0.9837;
0.9870; 0.9878; 0.9957. The Long Short Term Memory model performed a good predictive model for
wind speed in generally.
Keywords: Wind Speed, Long Short Term Memory, Turkey, Forecastin