Elazığ İli için Meterolojik Ölçüm Verileri Kullanılarak Rüzgar Hızı Tahmini

Year 2023, Volume: Vol:8 Issue: Issue:2, 110 - 120, 20.12.2023

Serdal Polat , Nuh Alpaslan , İbrahim Rıza Hallaç

https://doi.org/10.53070/bbd.1381841

Abstract

Enerji ihtiyacındaki artış ve çevresel kaygılar nedeniyle yenilenebilir enerji kaynaklarının küresel düzeydeki önemi giderek artmaktadır. Rüzgâr enerjisi, elektrik enerjisi üretiminde son yıllarda giderek daha fazla önem kazanmaktadır. Rüzgâr türbinlerin güvenli işletilmesi için rüzgâr hızı tahmini büyük önem taşımaktadır. Bu çalışmada, Elazığ ilinde farklı bölgelerden elde edilen veriler kullanılarak farklı modellerin rüzgar hızı tahmin başarıları incelenmiştir. Çalışmada, LSTM, rastgele orman ve XGBoost modelleri kullanılmıştır. Veri seti mevsimsellik ve trend bileşenleri STL yöntemiyle ayrıştırılmış ve Fourier dönüşümü ile mevsimsel bileşenler belirlenmiştir. Elde edilen sonuçlar, farklı bölgelerde farklı modellerin daha iyi performans gösterdiğini göstermektedir. Sonuçlara göre, Elazığ Merkez, Keban ve Sivrice bölgesinde XGBoost ve rastgele orman modelleri en düşük RMSE ve MSE değerlerine sahiptir, bu da bu modellerin bu bölge için daha iyi tahminler yaptığını göstermektedir.

Keywords

Hibrit model, LSTM, Random Forest, XGBoost, STL, FFT ve rüzgar hızı tahmini.

Wind Speed Prediction Using Meteorological Measurements for Elazığ Province

Abstract

As a result of the increasing energy demand and growing environmental concerns, the global significance of renewable energy resources is steadily rising. Wind energy has been increasingly gaining importance in electricity generation in recent years. The accurate prediction of wind speed is crucial for the safe operation of wind turbines. In this study, wind speed prediction performance of different models was examined using data obtained from various regions in the Elazığ province. LSTM, random forest, and XGBoost models were employed in the study. The dataset was decomposed into seasonal and trend components using the STL method, and seasonal components were determined using Fourier transformation. The results indicate that different models perform better in different regions. According to the findings, XGBoost and random forest models exhibit the lowest RMSE and MSE values in Elazığ, Keban, and Sivrice regions, indicating better predictions for these models in these areas.

Keywords

Hybrid model, LSTM, Random Forest, XGBoost, STL, FFT and wind speed prediction.

References

• Alkan, Ö., & Albayrak, Ö. K. (2020). Ranking of renewable energy sources for regions in Turkey by fuzzy entropy based fuzzy COPRAS and fuzzy MULTIMOORA. Renewable Energy, 162, 712–726. https://doi.org/10.1016/j.renene.2020.08.062
• Box, G. E. P. (1989). An Unexpected Route to Time Series.
• Breiman, L. (2001). Random Forests. Kluwer Academic Publishers, 45, 5–32.
• Brockwell, P. J., & Davis, R. A. (2002). Introduction to Time Series and Forecasting. In G. Casella, S. Fienberg, & I. Olkin (Eds.), Springer Texts in Statistics (Second Edi).
• Brownlee, J. (2023). How to Develop a Random Forest Ensemble in Python. https://machinelearningmastery.com/random-forest-ensemble-in-python/
• Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17-Augu, 785–794. https://doi.org/10.1145/2939672.2939785
• Du, P. (2019). Ensemble Machine Learning-Based Wind Forecasting to Combine NWP Output with Data from Weather Station. IEEE Transactions on Sustainable Energy, 10(4), 2133–2141. https://doi.org/10.1109/TSTE.2018.2880615
• Ghoniem, A. F. (2011). Needs, resources and climate change: Clean and efficient conversion technologies. Progress in Energy and Combustion Science, 37(1), 15–51. https://doi.org/10.1016/j.pecs.2010.02.006
• Gielen, D., Boshell, F., Saygin, D., Bazilian, M. D., Wagner, N., & Gorini, R. (2019). The role of renewable energy in the global energy transformation. Energy Strategy Reviews, 24(January), 38–50. https://doi.org/10.1016/j.esr.2019.01.006
• Hamzacebi, C., & Es, H. A. (2014). Forecasting the annual electricity consumption of Turkey using an optimized grey model. Energy, 70, 165–171. https://doi.org/10.1016/j.energy.2014.03.105
• Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
• Liaw, A., & Wiener, M. (2002). Classification and Regression by randomForest. R News, 2(3), 18–22.
• Liu, H., Mi, X., & Li, Y. (2018). Smart deep learning based wind speed prediction model using wavelet packet decomposition, convolutional neural network and convolutional long short term memory network. Energy Conversion and Management, 166, 120–131. https://doi.org/10.1016/J.ENCONMAN.2018.04.021
• Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Ben Taieb, S., Bergmeir, C., Bessa, R. J., Bijak, J., Boylan, J. E., Browell, J., Carnevale, C., Castle, J. L., Cirillo, P., Clements, M. P., Cordeiro, C., Cyrino Oliveira, F. L., De Baets, S., Dokumentov, A., … Ziel, F. (2022). Forecasting: theory and practice. International Journal of Forecasting, 38(3), 705–871. https://doi.org/10.1016/j.ijforecast.2021.11.001
• Prabha, P. P., Vanitha, V., & Resmi, R. (2019). Wind Speed Forecasting using Long Short Term Memory Networks. 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2019, 1310–1314. https://doi.org/10.1109/ICICICT46008.2019.8993279
• Yan, Y., Wang, X., Ren, F., Shao, Z., & Tian, C. (2022). Wind speed prediction using a hybrid model of EEMD and UKSB considering seasonal features. Energy Reports, 8, 8965–8980. https://doi.org/10.1016/j.egyr.2022.07.007