Ayrıştırma Yöntemlerinin Derin Öğrenme Algoritması ile Tanımlanan Rüzgâr Hızı Tahmin Modeli Başarımına Etkisinin İncelenmesi

Yıl 2020, Sayı: 20, 844 - 853, 31.12.2020

Aytaç Altan , Seçkin Karasu

https://doi.org/10.31590/ejosat.785699

Cited By: 5

Öz

Son on yılda, rüzgâr enerjisine dayalı yenilenebilir enerji kaynaklarının kullanımındaki kayda değer artış, bu kaynakların ihtiyaçlara kesintisiz ve tahmin edilebilir bir şekilde cevap verebilmesini sağlamak için rüzgâr hızı tahmin çalışmalarının önemini arttırmaktadır. Rüzgâr enerjisinden teknolojik olarak faydalanmak için; yararlanma imkânlarının bilinmesi, yüksek rüzgâr enerjisi potansiyeline sahip bölgelerin belirlenmesi, rüzgâr karakteristiklerinin ve hızlarının tahmin edilebilir olması oldukça önemlidir. Güvenilir ve yüksek hassasiyetli rüzgâr hızı tahmini, rüzgâr gücünün verimli kullanımı ve kullanılması açısından hayati önem arz etmektedir. Rüzgâr hızının durağan olmaması ve stokastik yapısı, rüzgâr hızı tahmininde ayrıştırma yöntemlerini ön plana çıkarmaktadır. Bu çalışmada, ayrıştırma yöntemlerinden ampirik kip ayrışımı, topluluk ampirik kip ayrışımı ve ampirik dalgacık dönüşümünün derin öğrenme yöntemlerinden uzun-kısa süreli bellek ile elde edilen rüzgar hızı tahmin modeli başarımına etkisi incelenmektedir. Türkiye'nin Marmara bölgesindeki üç rüzgâr istasyonundan toplanan veriler her bir ayrıştırma yöntemi ile alt bant sinyallerine ayrıştırılarak uzun-kısa süreli bellek model yapısı ile kombine rüzgâr hızı tahmin modeli oluşturulmaktadır. Her bir ayrıştırma yöntemi ile birlikte elde edilen kombine modellerin başarımları istatistiksel hata ölçütlerine göre değerlendirilmekte ve rüzgâr hızı tahmin modeli başarımına etkisi en yüksek ayrıştırma yöntemi, melez rüzgâr hızı tahmin modeli elde edilmesi çalışmalarında önerilmektedir.

Anahtar Kelimeler

Rüzgâr hızı, Tahmin modeli, Ayrıştırma yöntemleri, Derin öğrenme, Uzun-Kısa Süreli Bellek, Ampirik Dalgacık Dönüşümü

Investigation of the Effect of Decomposition Methods on Wind Speed Forecasting Model Performance Defined by Deep Learning Algorithm

Öz

In the last decade, the significant increase in the use of renewable energy sources based on wind energy has increased the importance of wind speed forecasting studies to ensure that these resources can respond to the needs in an uninterrupted and predictable manner. In order to be able to utility from wind energy technologically, it is very important to knowing the facilities of utilization, determining the regions, which have high potential of wind energy, being predictable the wind characteristics and speeds. The reliable and high accuracy wind speed forecasting is of vital to the efficient exploitation and utilization of wind power. The non-stationary and stochastic structure of the wind speed raise to the forefront the decomposition methods in wind speed forecasting. In this study, the effect of empirical mode decomposition, ensemble empirical mode decomposition and empirical wavelet transform on the performance of wind speed forecasting model obtained with long-short term memory from deep learning methods is investigated. The data collected from five wind farms in Marmara region, Turkey are decomposed to subband signal by these three decomposition methods, and the combined wind speed forecasting model is obtained with the long-short-term memory model structure. The performance of the combined models obtained by each decomposition method has been evaluated according to the statistical error criteria, and the decomposition method that is the highest effective to performance of wind speed forecasting model is suggested for the studies of obtaining the hybrid wind speed forecasting model.

Anahtar Kelimeler

Wind speed, Forecasting model, Decomposition methods, Deep learning, Long-Short Term Memory, Empirical Wavelet Transform

Kaynakça

• N. Scarlat, J. F. Dallemand, F. Monforti-Ferrario, M. Banja, and V. Motola, “Renewable Energy Policy Framework and Bioenergy Contribution in the European Union-An Overview from National Renewable Energy Action Plans and Progress Reports,” Renewable and Sustainable Energy Reviews, vol. 51, pp. 969-985, 2015.
• M. T. Çakır, “Türkiye’nin Rüzgâr Enerji Potansiyeli ve AB Ülkeleri içindeki Yeri”, Politeknik Dergisi, c. 13, s. 4, ss. 287-293, 2010.
• H. Liu, X. Mi, and Y. Li, “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, vol. 166, pp. 120-131, 2018.
• M. Lange and U. Focken, “Physical Approach to Short-Term Wind Power Prediction”, Berlin, Germany: Springer, 2006, ch. 3, pp. 22-38.
• M. G. De Giorgi, A. Ficarella, and M. Tarantino, “Assessment of the Benefits of Numerical Weather Predictions in Wind Power Forecasting based on Statistical Methods”, Energy, vol. 36, no. 7, pp. 3968-3978, 2011.
• S. Karasu, A. Altan, Z. Saraç, and R. Hacıoğlu, “Estimation of Fast Varied Wind Speed based on NARX Neural Network by using Curve Fitting”, International Journal of Energy Applications and Technologies, vol. 4, no. 3, pp. 137-146, 2017.
• G. Li and J. Shi, “On Comparing Three Artificial Neural Networks for Wind Speed Forecasting”, Applied Energy, vol. 87, no. 7, pp. 2313-2320, 2010.
• H. Li, J. Wang, H. Lu, and Z. Guo, “Research and Application of A Combined Model based on Variable Weight for Short Term Wind Speed Forecasting”, Renewable Energy, vol. 116, pp. 669-684, 2018.
• P. Du, J. Wang, Z. Guo, and W. Yang, “Research and Application of A Novel Hybrid Forecasting System based on Multi-Objective Optimization for Wind Speed Forecasting”, Energy Conversion and Management, vol. 150, pp. 90-107, 2017.
• G. H. Riahy and M. Abedi, “Short Term Wind Speed Forecasting for Wind Turbine Applications using Linear Prediction Method”, Renewable Energy, vol. 33, no. 1, pp. 35-41, 2008.
• H. Akçay and T. Filik, “Short-Term Wind Speed Forecasting by Spectral Analysis from Long-Term Observations with Missing Values”, Applied Energy, vol. 191, pp. 653-662, 2017.
• A. Sfetsos, “A Comparison of Various Forecasting Techniques Applied to Mean Hourly Wind Speed Time Series”, Renewable Energy, vol. 21, no. 1, pp. 23-35, 2000.
• E. Cadenas, W. Rivera, R. Campos-Amezcua, and C. Heard, “Wind Speed Prediction using A Univariate ARIMA Model and A Multivariate NARX Model”, Energies, vol. 9, no. 2, pp. 109-124, 2016.
• S. Karasu, A. Altan, Z. Saraç, and R. Hacıoğlu, “Prediction of Wind Speed with Non-Linear Autoregressive (NAR) Neural Networks”, 25th IEEE Signal Processing and Communications Applications Conference, pp. 1-4, Antalya, 2017.
• G. Li and J. Shi, “On Comparing Three Artificial Neural Networks for Wind Speed Forecasting”, Applied Energy, vol. 87, no. 7, pp. 2313-2320, 2010.
• M. Mohandes, S. Rehman, and S. M. Rahman, “Estimation of Wind Speed Profile using Adaptive Neuro-Fuzzy Inference System (ANFIS)”, Applied Energy, vol. 88, no. 11, pp. 4024-4032, 2011.
• H. Liu, H. Q. Tian, Y. F. Li, and L. Zhang, “Comparison of Four Adaboost Algorithm based Artificial Neural Networks in Wind Speed Predictions”, Energy Conversion and Management, vol. 92, pp. 67-81, 2015.
• W. Sun and Y. Wang, “Short-Term Wind Speed Forecasting based on Fast Ensemble Empirical Mode Decomposition, Phase Space Reconstruction, Sample Entropy and Improved Back-Propagation Neural Network”, Energy Conversion and Management, vol. 157, pp. 1-12, 2018.
• H. Liu, H. Q. Tian, X. F. Liang, and Y. F. Li, “Wind Speed Forecasting Approach using Secondary Decomposition Algorithm and Elman Neural Networks”, Applied Energy, vol. 157, pp. 183-194, 2015.
• J. Chen, G. Q. Zeng, W. Zhou, W. Du, and K. D. Lu, “Wind Speed Forecasting using Nonlinear-Learning Ensemble of Deep Learning Time Series Prediction and Extremal Optimization”, Energy Conversion and Management, vol. 165, pp. 681-695, 2018.
• H. Liu, C. Chen, H. Q. Tian, and Y. F. Li, “A Hybrid Model for Wind Speed Prediction using Empirical Mode Decomposition and Artificial Neural Networks”, Renewable Energy, vol. 48, pp. 545-556, 2012.
• H. Liu, X. W. Mi, and Y. F. Li, “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, vol. 166, pp. 120-131, 2018.
• H. Liu, X. W. Mi, and Y. F. Li, “Wind Speed Forecasting Method based on Deep Learning Strategy using Empirical Wavelet Transform, Long Short Term Memory Neural Network and Elman Neural Network”, Energy Conversion and Management, vol. 156, pp. 498-514, 2018.
• H. Liu, H. Tian, X. Liang, and Y. Li, “New Wind Speed Forecasting Approaches using Fast Ensemble Empirical Model Decomposition, Genetic Algorithm, Mind Evolutionary Algorithm and Artificial Neural Networks”, Renewable Energy, vol. 83, pp. 1066-1075, 2015.
• X. Ma, Y. Jin, and Q. Dong, “A Generalized Dynamic Fuzzy Neural Network based on Singular Spectrum Analysis Optimized by Brain Storm Optimization for Short-Term Wind Speed Forecasting”, Applied Soft Computing, vol. 54, pp. 296-312, 2017.
• C. Yu, Y. Li, and M. Zhang, “Comparative Study on Three New Hybrid Models using Elman Neural Network and Empirical Mode Decomposition based Technologies Improved by Singular Spectrum Analysis for Hour-Ahead Wind Speed Forecasting”, Energy Conversion and Management, vol. 147, pp. 75-85, 2017.
• Y. Jiang and G. Huang, “Short-Term Wind Speed Prediction: Hybrid of Ensemble Empirical Mode Decomposition, Feature Selection and Error Correction”, Energy Conversion and Management, vol. 144, pp. 340-350, 2017.
• Y. L. Hu and L. Chen, “A Nonlinear Hybrid Wind Speed Forecasting Model using LSTM Network, Hysteretic ELM and Differential Evolution Algorithm”, Energy Conversion and Management, vol. 173, pp. 123-142, 2018.
• N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, “The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis”, Proceedings of the Royal Society of London, Series A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903-995, 1998.
• X. Zhang, K. K. Lai, and S. Y. Wang, “A New Approach for Crude Oil Price Analysis based on Empirical Mode Decomposition”, Energy Economics, vol. 30, no. 3, pp. 905-918, 2008.
• N. E. Huang, M. L. C. Wu, S. R. Long, S. S. Shen, W. Qu, P. Gloersen, and K. L. Fan, “A Confidence Limit for the Empirical Mode Decomposition and Hilbert Spectral Analysis”, Proceedings of the Royal Society of London, Series A: Mathematical, Physical and Engineering Sciences, vol. 459, no. 2037, pp. 2317-2345, 2003.
• Z. Wu and N. E. Huang, “Ensemble Empirical Mode Decomposition: A Noise-Assisted Data Analysis Method”, Advances in Adaptive Data Analysis, vol. 1, no. 01, pp. 1-41, 2009.
• J. Gilles, “Empirical wavelet transform”, IEEE Transactions on Signal Processing, vol. 61 no. 16, pp. 3999-4010, 2013.
• S. Hochreiter and J. Schmidhuber, “Long short-term memory”, Neural Computation, vol. 9 no. 8, pp. 1735-1780, 1997.
• T. N. Sainath, O. Vinyals, A. Senior, and H. Sak, “Convolutional, long short-term memory, fully connected deep neural networks”, IEEE International Conference on Acoustics, Speech and Signal Processing, Brisbane, Australia, 2015, pp. 4580-4584.