Türkiye’deki Jeotermal Enerji Santrallerinin Kümülatif Kurulu Gücünün Yapay Sinir Ağı ve İki Yönlü Uzun-Kısa Vadeli Bellek Kullanılarak Tahmini

Year 2022, Issue: 34, 280 - 284, 31.03.2022

Mehmet Hakan Özdemir , Batin Latif Aylak

https://doi.org/10.31590/ejosat.1080608

Abstract

Türkiye büyük bir yenilenebilir enerji potansiyeline sahiptir. Yenilenebilir enerji kaynaklarından elektrik üreten santrallerin sayısı ve buna bağlı olarak kurulu güç yıllar içinde artış göstermiştir. Aralık 2021 sonu itibarıyla Türkiye'nin kümülatif kurulu gücü 99819,6 MW'a ulaşmıştır ve yenilenebilir enerji kaynaklarından elektrik üreten enerji santrallerinin toplam kurulu güç içindeki payı %53,72 olmuştur. Kurulu güç artmasına rağmen toplam elektrik üretiminde, yenilenebilir enerji kaynakları kullanan enerji santrallerinin oranı henüz istenen düzeyde değildir. Bununla birlikte, jeotermal enerji, en çok bilinen diğer yenilenebilir enerji türlerinin yanı sıra elektrik üretiminde giderek daha fazla kullanılmaktadır. Türkiye'de jeotermal enerji santrallerinin (JES) kurulu gücünün 2007 yılından sonra yavaş yavaş artmaya başladığı ve Aralık 2021 sonunda kümülatif kurulu gücün 1676,2 MW'a ulaştığı görülmektedir. Bu çalışmada, Türkiye'deki JES'lerin 2007-2021 dönemindeki kümülatif kurulu gücü verileriyle, Yapay Sinir Ağı ve İki Yönlü Uzun-Kısa Vadeli Bellek kullanılarak Türkiye'deki JES'lerin 2022 yılı kümülatif kurulu gücü tahmin edilmiştir ve sonuçlar karşılaştırılarak yorumlanmıştır.

Keywords

kurulu güç, jeotermal enerji, tahmin, yapay sinir ağı

Prediction of Cumulative Installed Power of Geothermal Power Plants in Turkey by Using Artificial Neural Network and Bidirectional Long Short-Term Memory

Abstract

Turkey has a great potential for renewable energies. The number of power plants (PP) producing electricity from renewable energy sources and accordingly the installed power has risen over the years. As of the end of December 2021, the cumulative installed power of Turkey reached 99819.6 MW and the share of the total installed power of the PPs generating electricity from renewable energy sources was 53.72%. Although the installed power has increased, the percentage of PPs using renewable energy sources in total electricity generation is not yet at the desired level. However, geothermal energy is being used more and more in electricity generation alongside the other most well-known types of renewable energy. It can be observed that the installed power of geothermal power plants (GPP) in Turkey started to increase gradually after 2007, and as of the end of December 2021, the cumulative installed power reached 1676.2 MW. In this study, with the data for the cumulative installed power of GPPs in Turkey in the 2007-2021 period, the cumulative installed power of GPPs in Turkey for 2022 was predicted by using Artificial Neural Network (ANN) and Bidirectional Long Short-Term Memory (BLSTM) methods, and the results were compared and interpreted.

Keywords

installed power, geothermal energy, prediction, artificial neural network

References

• K. Kaya, M. C. Şenel and E. Koç , "Dünyada ve Türkiye’de Yenilenebilir Enerji Kaynaklarının Değerlendirilmesi", Technological Applied Sciences, vol. 13, no. 3, pp. 219-234, Jul. 2018.
• Y. Akça and A. Kamacı, “TR81 Bölgesinin Yenilenebilir Enerji Durumu”, Bartın Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, vol 12, no. 24, pp. 400-412, 2021.
• D. G. Demir and M. Canbaz, “Türkiye’de 2001-2020 Yılları Arasında Devlet Teşviklerinin Yenilenebilir Enerji Sektörü Üzerine Etkisinin Değerlendirilmesi: Türkiye’de 2001-2020 Yılları Arasında Devlet Teşviklerinin Yenilenebilir Enerji Sektörü Üzerine Etkisinin Değerlendirilmesi,” Türk Kamu Yönetimi Dergisi, vol. 2, no. 2, 2021.
• C. Hakyemez (2022) Available: TSKB Ekonomik Araştırmalar Aylık Enerji Bülteni Aralık 2021. Available: https://www.tskb.com.tr/i/assets/document/pdf/enerji-bulteni-aralik-2021.pdf.
• TEİAŞ (2021a). Aralık 2021 Kurulu güç raporu. Available: https://www.teias.gov.tr/tr-TR/kurulu-guc-raporlari.
• TEİAŞ (2021b). Türkiye'nin Yenilenebilir Kaynaklarına Ait Kurulu Gücünün Toplam Kurulu Güç İçindeki Payının Yıllar İtibariyle Gelişimi (2000-2020). Available: https://www.teias.gov.tr/tr-TR/turkiye-elektrik-uretim-iletim-istatistikleri
• Y. Shi, X. Song, and G. Song, “Productivity prediction of a multilateral-well geothermal system based on a long short-term memory and multi-layer perceptron combinational neural network,” Applied Energy, vol. 282, p. 116046, Jan. 2021.
• H. Gudmundsdottir and R. N. Horne (2020, February). “Prediction modeling for geothermal reservoirs using deep learning” In 45th workshop on geothermal reservoir engineering. Stanford, California: Stanford University, 2020, pp. 1-12.
• A. Jiang, Z. Qin, T. T. Cladouhos, D. Faulder, and B. Jafarpour, “A Multiscale Recurrent Neural Network Model for Long-Term Prediction of Geothermal Energy Production”, In 47th Workshop on Geothermal Reservoir Engineering Stanford University, Stanford, California, 2022, pp. 1-8.
• M. B. Diaz and K. Y. Kim, “Improving rate of penetration prediction by combining data from an adjacent well in a geothermal project,” Renewable Energy, vol. 155, pp. 1394–1400, Aug. 2020.
• A. Yuswandari, A. Prayoga, and D. Purba, “Rate of penetration (ROP) prediction using artificial neural network to predict ROP for nearby well in a geothermal field,” Proc. 44th Work. Geotherm. Reserv. Eng. Stanford Univ. Stanford, California, Febr. 11, vol. 13, no. 2019, pp. 1–5.
• M. Diaz, K. Y. Kim, J. Lee, and H.-S. Shin, “Prediction of rate of penetration with data from adjacent well using artificial neural network,” in Geotechnics for Sustainable Infrastructure Development, Springer, 2020, pp. 517–522.
• D. Pérez-Zárate, E. Santoyo, A. Acevedo-Anicasio, L. Díaz-González, and C. García-López, “Evaluation of artificial neural networks for the prediction of deep reservoir temperatures using the gas-phase composition of geothermal fluids,” Computers & Geosciences, vol. 129, pp. 49–68, 2019.
• F. S. Tut Haklidir and M. Haklidir, “Prediction of reservoir temperatures using hydrogeochemical data, Western Anatolia geothermal systems (Turkey): a machine learning approach,” Natural Resources Research, vol. 29, no. 4, pp. 2333–2346, 2020.
• S. Jalilinasrabady, T. Tanaka, R. Itoi, and H. Goto, “Numerical simulation and production prediction assessment of Takigami geothermal reservoir,” Energy, vol. 236, p. 121503, 2021.
• G. Coro and E. Trumpy, “Predicting geographical suitability of geothermal power plants,” Journal of Cleaner Production, vol. 267, p. 121874, Sep. 2020.
• L. Mao and Z. Zhang, “Transient temperature prediction model of horizontal wells during drilling shale gas and geothermal energy,” Journal of Petroleum Science and Engineering, vol. 169, pp. 610–622, 2018.
• B. Baruque, S. Porras, E. Jove, and J. L. Calvo-Rolle, “Geothermal heat exchanger energy prediction based on time series and monitoring sensors optimization,” Energy, vol. 171, pp. 49–60, 2019.
• L. Zhang, S. Geng, J. Chao, L. Yang, Z. Zhao, G. Qin, and S. Ren, “Scaling and blockage risk in geothermal reinjection wellbore: Experiment assessment and model prediction based on scaling deposition kinetics,” Journal of Petroleum Science and Engineering, vol. 209, p. 109867, 2022.
• M. Hemmat Esfe, S. Saedodin, N. Sina, M. Afrand, and S. Rostami, “Designing an artificial neural network to predict thermal conductivity and dynamic viscosity of ferromagnetic nanofluid,” International Communications in Heat and Mass Transfer, vol. 68, pp. 50–57, Nov. 2015.
• Ş. Öztürk and U. Özkaya, “Skin lesion segmentation with improved convolutional neural network.” Journal of digital imaging, 33(4), 958-970, 2020.
• R. Ahmed, M. El Sayed, S. A. Gadsden, J. Tjong, and S. Habibi, “Automotive internal-combustion-engine fault detection and classification using artificial neural network techniques,” IEEE Transactions on vehicular technology, vol. 64, no. 1, pp. 21–33, 2014.
• E. Byvatov, U. Fechner, J. Sadowski, and G. Schneider, “Comparison of support vector machine and artificial neural network systems for drug/nondrug classification,” Journal of chemical information and computer sciences, vol. 43, no. 6, pp. 1882–1889, 2003.
• D. C. Park, M. A. El-Sharkawi, R. J. Marks, L. E. Atlas, and M. J. Damborg, “Electric load forecasting using an artificial neural network,” IEEE transactions on Power Systems, vol. 6, no. 2, pp. 442–449, 1991.
• J. L. Ticknor, “A Bayesian regularized artificial neural network for stock market forecasting,” Expert systems with applications, vol. 40, no. 14, pp. 5501–5506, 2013.
• A. Khosravi, R. N. N. Koury, L. Machado, and J. J. G. Pabon, “Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system,” Sustainable Energy Technologies and Assessments, vol. 25, pp. 146–160, 2018.
• S. Özden and A. Öztürk, “Yapay sinir ağları ve zaman serileri yöntemi ile bir endüstri alanının (ivedik OSB) elektrik enerjisi ihtiyaç tahmini,” Bilişim Teknolojileri Dergisi, vol. 11, no. 3, pp. 255–261, 2018.
• L. Deng and D. Yu, “Deep learning: methods and applications,” Foundations and trends in signal processing, vol. 7, no. 3–4, pp. 197–387, 2014.
• Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature, vol. 521, no. 7553, pp. 436–444, 2015.
• A. Graves and J. Schmidhuber, “Framewise phoneme classification with bidirectional LSTM and other neural network architectures,” Neural networks, vol. 18, no. 5–6, pp. 602–610, 2005.
• E. Kiperwasser and Y. Goldberg, “Simple and accurate dependency parsing using bidirectional LSTM feature representations,” Transactions of the Association for Computational Linguistics, vol. 4, pp. 313–327, 2016.