RÜZGÂR GÜCÜ TAHMİNİNDE UZUN KISA-SÜRELİ BELLEK: VERİ ÖRNEKLEME VE KÜMELEMENİN ETKİSİ

Volkan Yamaçlı

doi:10.17780/ksujes.1535461

TR EN

RÜZGÂR GÜCÜ TAHMİNİNDE UZUN KISA-SÜRELİ BELLEK: VERİ ÖRNEKLEME VE KÜMELEMENİN ETKİSİ

Öz

Rüzgâr enerjisi, temiz, yenilenebilir ve çevre dostu olarak geleneksel güç kaynaklarının en verimli alternatiflerinden biridir. Bununla birlikte, rüzgâr hızının ve dolayısıyla güç kalitesinin değişken doğasından dolayı, elektrik şebekesinin güvenliği ve güvenilirliğinin önünde bazı engeller oluşabilmektedir. Rüzgâr hızı ve gücü tahmini aracılığı ile güç planlaması sorununu çözebilmek için, en popüler yinelemeli sinir ağlarından (YNSA) biri olan uzun kısa-süreli bellek (UKSB) tabanlı bir tahmin modeli önerilmektedir. Bu çalışmada Türkiye’de mevcut olan bir rüzgâr türbininden elde edilen ve yayımlanan bir veri seti kullanılmıştır. İlk olarak UKSB ağı, rüzgâr hızı ve rüzgâr gücü zaman-dizilerine ilişkin farklı pencere boyutundaki veriler için eğitilmiştir. Daha sonra bu iki UKSB ağının çıktıları başka bir UKSB ağı için girdi olarak kullanılarak daha yüksek aralıklarla daha az miktarda veri için sağlam bir yaklaşım sağlanması hedeflenmiştir. Nihai rüzgâr gücü tahmin verileri, her bir dizinin sonuçları kullanılarak elde edilir. 30-dakikalık, 1-saatik, 6-saatlik ve 1-günlük aralıklarla 4 farklı durum çalışması yapılarak önerilen algoritmanın etkinliği gösterilmiştir.

Anahtar Kelimeler

References

Abaci, K., Yetgin, Z., Yamacli, V., & Isiker, H. (2024). Modified effective butterfly optimizer for solving optimal power flow problem. Heliyon, 10(12), e32862. https://doi.org/10.1016/j.heliyon.2024.e32862
Ai, X., Li, S., & Xu, H. (2022). Short-term wind speed forecasting based on two-stage preprocessing method, sparrow search algorithm and long short-term memory neural network. Energy Reports, 8, 14997-15010. https://doi.org/10.1016/j.egyr.2022.11.051
Akçay, H., & Yıltas-Kaplan, D. (2024). Zaman serileri tahminleme algoritmaları ile kontör tüketim tahminlemesi ve karşılaştırmalı uygulaması. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(1), 166-189. https://doi.org/10.17780/ksujes.1369811
Al‐Shaikhi, A., Nuha, H., Mohandes, M., Rehman, S., & Adrian, M. (2022). Vertical wind speed extrapolation model using long short‐term memory and particle swarm optimization. Energy Science & Engineering, 10(12), 4580-4594. https://doi.org/10.1002/ese3.1291
Bokde, N., Feijóo, A., Villanueva, D., & Kulat, K. (2019). A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction. Energies, 12(2), 254. https://doi.org/10.3390/en12020254
Chen, J., Guo, Z., Zhang, L., & Zhang, S. (2024). Short-term wind speed prediction based on improved Hilbert–Huang transform method coupled with NAR dynamic neural network model. Scientific Reports, 14(1), 617. https://doi.org/10.1038/s41598-024-51252-y
Erisen, B. (2018). Wind Turbine Scada Dataset. https://www.kaggle.com/datasets/berkerisen/wind-turbine-scada-dataset
Gan, Z., Li, C., Zhou, J., & Tang, G. (2021). Temporal convolutional networks interval prediction model for wind speed forecasting. Electric Power Systems Research, 191, 106865. https://doi.org/10.1016/j.epsr.2020.106865

Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to Forget: Continual Prediction with LSTM. Neural Computation, 12(10), 2451-2471. https://doi.org/10.1162/089976600300015015
Guan, S., Wang, Y., Liu, L., Gao, J., Xu, Z., & Kan, S. (2023). Ultra-short-term wind power prediction method combining financial technology feature engineering and XGBoost algorithm. Heliyon, 9(6), e16938. https://doi.org/10.1016/j.heliyon.2023.e16938
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
Hou, X., Hu, W., & Luo, M. (2024). Short-term wind farm cluster power point-interval prediction based on graph spatio-temporal features and S-Stacking combined reconstruction. Heliyon, 10(14), e33945. https://doi.org/10.1016/j.heliyon.2024.e33945
Jun Zhang, & Man, K. F. (1998). Time series prediction using RNN in multi-dimension embedding phase space. SMC’98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218), 1868-1873. https://doi.org/10.1109/ICSMC.1998.728168
Jung, J., & Broadwater, R. P. (2014). Current status and future advances for wind speed and power forecasting. Renewable and Sustainable Energy Reviews, 31, 762-777. https://doi.org/10.1016/j.rser.2013.12.054
Lawal, A., Rehman, S., Alhems, L. M., & Alam, Md. M. (2021). Wind Speed Prediction Using Hybrid 1D CNN and BLSTM Network. IEEE Access, 9, 156672-156679. https://doi.org/10.1109/ACCESS.2021.3129883
Li, M., Zhang, Z., Ji, T., & Qu, Q. H. (2020). Ultra-short term wind speed prediction using mathematical morphology decomposition and long short-term memory. CSEE Journal of Power and Energy Systems. https://doi.org/10.17775/CSEEJPES.2019.02070
Lopez, L., Oliveros, I., Torres, L., Ripoll, L., Soto, J., Salazar, G., & Cantillo, S. (2020). Prediction of Wind Speed Using Hybrid Techniques. Energies, 13(23), 6284. https://doi.org/10.3390/en13236284
Memarzadeh, G., & Keynia, F. (2021). Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm. Electric Power Systems Research, 192, 106995. https://doi.org/10.1016/j.epsr.2020.106995
Pavlov-Kagadejev, M., Jovanovic, L., Bacanin, N., Deveci, M., Zivkovic, M., Tuba, M., Strumberger, I., & Pedrycz, W. (2024). Optimizing long-short-term memory models via metaheuristics for decomposition aided wind energy generation forecasting. Artificial Intelligence Review, 57(3), 45. https://doi.org/10.1007/s10462-023-10678-y
Shao, B., Song, D., Bian, G., & Zhao, Y. (2021). Wind Speed Forecast Based on the LSTM Neural Network Optimized by the Firework Algorithm. Advances in Materials Science and Engineering, 2021, 1-13. https://doi.org/10.1155/2021/4874757
Shi, Z., Yao, W., Li, Z., Zeng, L., Zhao, Y., Zhang, R., Tang, Y., & Wen, J. (2020). Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions. Applied Energy, 278, 115733. https://doi.org/10.1016/j.apenergy.2020.115733
Sun, W., & Wang, Y. (2018). 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, 157, 1-12. https://doi.org/10.1016/j.enconman.2017.11.067
Wang, C., Han, D., Liu, Q., & Luo, S. (2019). A Deep Learning Approach for Credit Scoring of Peer-to-Peer Lending Using Attention Mechanism LSTM. IEEE Access, 7, 2161-2168. https://doi.org/10.1109/ACCESS.2018.2887138
Wang, J., Sun, J., & Zhang, H. (2013). Short-term wind power forecasting based on support vector machine. 2013 5th International Conference on Power Electronics Systems and Applications(PESA), 1-5. https://doi.org/10.1109/PESA.2013.6828211
Wang, J., Wang, Y., Li, Z., Li, H., & Yang, H. (2020). A combined framework based on data preprocessing, neural networks and multi-tracker optimizer for wind speed prediction. Sustainable Energy Technologies and Assessments, 40, 100757. https://doi.org/10.1016/j.seta.2020.100757
Xie, Y., Li, C., Tang, G., & Liu, F. (2021). A novel deep interval prediction model with adaptive interval construction strategy and automatic hyperparameter tuning for wind speed forecasting. Energy, 216, 119179. https://doi.org/10.1016/j.energy.2020.119179
Yamaçli, V., Işiker, H., Yetgín, Z., & Abaci, K. (2024). Solving Optimal Power Flow Control Problem Using Honey Formation Optimization Algorithm. IEEE Access, 12, 109293-109322. https://doi.org/10.1109/ACCESS.2024.3439021

Details

Primary Language

Turkish

Subjects

Deep Learning , Electrical Energy Generation (Incl. Renewables, Excl. Photovoltaics)

Journal Section

Research Article

Authors

Volkan Yamaçlı ^*
0000-0003-0331-8818
Türkiye

Publication Date

March 3, 2025

Submission Date

August 19, 2024

Acceptance Date

September 13, 2024

Published in Issue

Year 1970 Volume: 28 Number: 1

DOI

https://doi.org/10.17780/ksujes.1535461

IZ

https://izlik.org/JA88MF25ZP

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APA

Yamaçlı, V. (2025). RÜZGÂR GÜCÜ TAHMİNİNDE UZUN KISA-SÜRELİ BELLEK: VERİ ÖRNEKLEME VE KÜMELEMENİN ETKİSİ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 202-215. https://doi.org/10.17780/ksujes.1535461

LONG SHORT-TERM MEMORY FOR WIND POWER ESTIMATION: THE EFFECT OF DATA SAMPLING AND CLUSTERING

RÜZGÂR GÜCÜ TAHMİNİNDE UZUN KISA-SÜRELİ BELLEK: VERİ ÖRNEKLEME VE KÜMELEMENİN ETKİSİ

Öz

Anahtar Kelimeler

LONG SHORT-TERM MEMORY FOR WIND POWER ESTIMATION: THE EFFECT OF DATA SAMPLING AND CLUSTERING

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