TY  - JOUR
T1  - A New Artificial Neural Network Based Power Estimation Study for Wind Energy Systems
AU  - Taşdemir, Bahtiyar
AU  - Yaz, Mustafa
PY  - 2025
DA  - November
Y2  - 2025
DO  - 10.29137/ijerad.1672778
JF  - International Journal of Engineering Research and Development
JO  - IJERAD
PB  - Kirikkale University
WT  - DergiPark
SN  - 1308-5506
SP  - 567
EP  - 576
VL  - 17
IS  - 3
LA  - en
AB  - Today, the demand for electrical energy is constantly increasing, primarily due to the advances in the industrial sector. This increase in demand has made wind energy a prominent option in the search for alternative energy sources due to its low investment costs and environmental friendliness. However, accurate forecasting methods are needed due to the variability of wind energy production affected by meteorological data. Including additional parameters besides the existing meteorological data could help improve the accuracy of these forecasts. This study explores the impact of the particulate matter (PM10) parameter on wind energy prediction through the employment of an artificial neural network (ANN) model. The comparison of prediction results based on Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) demonstrates that, when it comes to the daily wind power prediction of the PM10 parameter, the prediction model based on the artificial neural network (ANN) makes a significant contribution.
KW  - Artificial neural network
KW  - forecasting
KW  - optimization
KW  - wind power generation
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UR  - https://doi.org/10.29137/ijerad.1672778
L1  - https://dergipark.org.tr/en/download/article-file/4758162
ER  -