A New Artificial Neural Network Based Power Estimation Study for Wind Energy Systems

Bahtiyar Taşdemir; Mustafa Yaz

doi:10.29137/ijerad.1672778

A New Artificial Neural Network Based Power Estimation Study for Wind Energy Systems

Abstract

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.

Keywords

Artificial neural network , forecasting , optimization , wind power generation

References

Abdoos, A. A. (2016). A new intelligent method based on a combination of VMD and ELM for short-term wind power forecasting. Neurocomputing, 203, 111–120. https://doi.org/10.1016/j.neucom.2016.03.054
Ağbulut, Ü., Gürel, A. E., & Biçen, Y. (2021). Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison. Renewable and Sustainable Energy Reviews, 135, 110114. https://doi.org/10.1016/j.rser.2020.110114
Anderson, D., & McNeill, G. (1992). Artificial neural networks technology. Kaman Sciences Corporation.
Caudill, M. (1987). Neural network part 1. AI Expert, 2(12), 46–52.
Ding, Y., Chen, Z., Zhang, H., Wang, X., & Guo, Y. (2022). A short-term wind power prediction model based on CEEMD and WOAKELM. Renewable Energy, 189, 188–198. https://doi.org/10.1016/j.renene.2022.02.108
Du, P., Wang, J., Yang, W., & Niu, T. (2019). A novel hybrid model for short-term wind power forecasting. Applied Soft Computing, 80, 93–106. https://doi.org/10.1016/j.asoc.2019.03.035
Dubey, N., & Pervez, S. (2008). Investigation of variation in ambient PM10 levels within an urban-industrial environment. Aerosol and Air Quality Research, 8(1), 54–64. https://doi.org/10.4209/aaqr.2007.07.0033
Farah, S., David, A. W., Humaira, N., Aneela, Z., & Steffen, E. (2022). Short-term multi-hour ahead country-wide wind power prediction for Germany using gated recurrent unit deep learning. Renewable and Sustainable Energy Reviews, 167, 112700. https://doi.org/10.1016/j.rser.2022.112700
Global Wind Energy Council. (2025). GWEC. https://gwec.net Date of acces: 10 February 2025
Guo, N.-Z., Shi, K.-Z., Li, B., Qi, L.-W., Wu, H.-H., Zhang, Z.-L., & Xu, J.-Z. (2022). A physics-inspired neural network model for short-term wind power prediction considering wake effects. Energy, 261, 125208. https://doi.org/10.1016/j.energy.2022.125208

Jiang, T., & Liu, Y. (2023). A short-term wind power prediction approach based on ensemble empirical mode decomposition and improved long short-term memory. Computers and Electrical Engineering, 110, 108830. https://doi.org/10.1016/j.compeleceng.2023.108830
Karakoç, O., & Ekercin, S. (2024). Generation of PM10 map with Sentinel-2 satellite images: The case of Çankırı Province. Afyon Kocatepe University Journal of Sciences and Engineering, 24(2), 350–359. https://doi.org/10.35414/akufemubid.1372761
Koç, M. L., Balas, C. E., & Arslan, A. (2004). Preliminary design of rock-filled breakwaters using artificial neural networks. Technical Journal, 15(74).
Kohonen, T. (1987, June 21). State of the art in neural computing. In IEEE First International Conference on Neural Networks.
Lever, J., Krzywinski, M., & Altman, N. (2016). Model selection and overfitting. Nature Methods, 13(9), 703–704. https://doi.org/10.1038/nmeth.3968
Li, Z., Luo, X., Liu, M., Cao, X., Du, S., & Sun, H. (2022). Short-term prediction of the power of a new wind turbine based on IAOLSTM. Energy Reports, 8, 9025–9037. https://doi.org/10.1016/j.egyr.2022.07.030
Li, Z., Ye, L., Zhao, Y., Pei, M., Lu, P., Li, Y., & Dai, B. (2023). A spatiotemporal directed graph convolution network for ultra-shortterm wind power prediction. IEEE Transactions on Sustainable Energy, 14(1), 39–54. https://doi.org/10.1109/TSTE.2022.3198816
Liao, X., Liu, Z., Zheng, X., Ping, Z., & He, X. (2023). Wind power prediction based on periodic characteristic decomposition and a multilayer attention network. Neurocomputing, 534, 119–132. https://doi.org/10.1016/j.neucom.2023.02.061
Meng, A., Chen, S., Ou, Z., Xiao, J., Zhang, J., Chen, S., Zhang, Z., Liang, R., Zhang, Z., Xian, Z., Wang, C., Yin, H., & Yan, B. (2022). A novel few-shot learning approach for wind power prediction applying secondary evolutionary generative adversarial network. Energy, 261, 125276. https://doi.org/10.1016/j.energy.2022.125276
Öztemel, E. (2006). Artificial neural networks (2nd ed.). Papatya Publishing.
Öztürk, M., Kayabaşı, R., & Taşdemir, O. (2025). Wind energy potential of kırşehir and power analysis of wind power plants in the central anatolia region. Kahramanmaraş Sütçü İmam University Journal of Engineering Sciences, 28(1), 189-201. https://doi.org/10.17780/ksujes.1533576
Ray, S. S., Verma, R. K., Singh, A., Ganesapillai, M., & Kwon, Y.-N. (2023). A holistic review of how artificial intelligence has redefined water treatment and seawater desalination processes. Desalination, 546, 116221. https://doi.org/10.1016/j.desal.2022.116221
Saraç, T. (2005). Estimating software project duration using artificial neural networks [Master's thesis, Gazi University Institute of Natural Sciences].
Teke, A., Yıldırım, H. B., & Çelik, Ö. (2015). Evaluation and performance comparison of different models for the estimation of solar radiation. Renewable and Sustainable Energy Reviews, 50, 1097–1107. https://doi.org/10.1016/j.rser.2015.05.049
Türkiye Elektrik İletim A.Ş. (2025). TEİAŞ. https://www.teias.gov.tr
Wang, J., Zhu, H., Cheng, F., Zhou, C., Zhang, Y., Xu, H., & Liu, M. (2023). A novel wind power prediction model improved with feature enhancement and autoregressive error compensation. Journal of Cleaner Production, 420, 138386. https://doi.org/10.1016/j.jclepro.2023.138386
Wang, L., & He, Y. (2022). M2STAN: Multi-modal multi-task spatiotemporal attention network for multi-location ultra-short-term wind power multi-step predictions. Applied Energy, 324, 119672. https://doi.org/10.1016/j.apenergy.2022.119672
Xiao, L., Qian, F., & Shao, W. (2017). Multi-step wind speed forecasting based on a hybrid forecasting architecture and an improved bat algorithm. Energy Conversion and Management, 143, 410–430. https://doi.org/10.1016/j.enconman.2017.04.012
Yamaçlı, V. (2025). Long-term and short-term memory in wind power forecasting: the effect of data sampling and clustering. Kahramanmaraş Sütçü İmam University Journal of Engineering Sciences, 28(1), 202-215. https://doi.org/10.17780/ksujes.1535461
Zhang, F., Li, N., Li, L., Wang, S., & Du, C. (2023). A local semi-supervised ensemble learning strategy for the data‐driven soft sensor of the power prediction in wind power generation. Fuel, 333, 126435. https://doi.org/10.1016/j.fuel.2022.126435
Zhao, X., Wang, C., Su, J., & Wang, J. (2019). Research and application based on the swarm intelligence algorithms and artificial intelligence for wind farm decision systems. Renewable Energy, 134, 681–697. https://doi.org/10.1016/j.renene.2018.11.061

Details

Primary Language

English

Subjects

Electrical Energy Generation (Incl. Renewables, Excl. Photovoltaics) , Electrical Engineering (Other)

Journal Section

Research Article

Authors

Bahtiyar Taşdemir ^*
0000-0001-7335-5185
Türkiye

Mustafa Yaz
0000-0001-7042-7649
Türkiye

Early Pub Date

November 23, 2025

Publication Date

November 30, 2025

Submission Date

April 9, 2025

Acceptance Date

June 30, 2025

Published in Issue

Year 2025 Volume: 17 Number: 3

DOI

https://doi.org/10.29137/ijerad.1672778

IZ

https://izlik.org/JA76DL64DW

APA

Taşdemir, B., & Yaz, M. (2025). A New Artificial Neural Network Based Power Estimation Study for Wind Energy Systems. International Journal of Engineering Research and Development, 17(3), 567-576. https://doi.org/10.29137/ijerad.1672778

Kırıkkale University, Faculty of Engineering and Natural Science, 71450 Yahşihan / Kırıkkale, Türkiye.

ijerad@kku.edu.tr

A New Artificial Neural Network Based Power Estimation Study for Wind Energy Systems

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Early Pub Date

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite