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Journals / Business and Management Studies: An International Journal / 2020 / Cilt: 8 - Sayı: 5

INSTALLED SOLAR POWER PREDICTION FOR TURKEY USING ARTIFICIAL NEURAL NETWORK AND BIDIRECTIONAL LONG SHORT-TERM MEMORY

TÜRKİYE İÇİN GÜNEŞ ENERJİSİ KURULU GÜCÜNÜNYAPAY SİNİR AĞI VEİKİ YÖNLÜ UZUN- KISA VADELİ BELLEKKULLANILARAK TAHMİNİ

Murat İNCE
Batin Latif AYLAK
Mehmet Hakan ÖZDEMİR
Okan ORAL
Pages
4047–4068
Publication Date
2022-11-06
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The cumulative installed power was predicted, and the results were compared and interpreted.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Renewable energy sources play an essentialrole insustainable development.
📊
Key Findings & Data:Nevertheless, with the increase in installed power in recent years, electricity generation from solar energy has gained momentum.
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Conclusion & Impact:The cumulative installed power was predicted, and the results were compared and interpreted.
🔬Design:Research Article
📍Scope:2009-2019 Verileri
⚙️Variables:Domain Parameters

Abstract

Renewable energy sources play an essentialrole insustainable development. The share of renewable energy-based energy generation is rapidly increasing all over the world. Turkey has a great potential in terms of both solar and wind energy due to its geographical location. The desired level has not yet been reached in using this potential. Nevertheless, with the increase in installed power in recent years, electricity generation from solar energy has gained momentum. In this study, data on cumulative installed solar power in Turkey in the 2009-2019 period were used. Artificial Neural Network (ANN) and Bidirectional Long Short-Term Memory (BLSTM) methods were selected to predict the cumulative installed solar power for 2020 with these data. The cumulative installed power was predicted, and the results were compared and interpreted.

Özet

Sürdürülebilir bir kalkınma için yenilenebilir enerji kaynakları önemli bir rol oynamakta ve yenilenebilir enerji kaynaklı enerji üretiminin payı tüm dünyada hızla artmaktadır. Ülkemiz, bulunduğu coğrafi konumu nedeniyle hem güneş hem de rüzgâr enerjisi açısından büyük bir potansiyele sahiptir. Bu potansiyeli kullanma konusunda henüz stenen düzeye ulaşılamamıştır. Yine de son yıllarda kurulu gücün artmasıyla birlikte güneş enerjisinden elektrik üretimi çalışmaları hız kazanmıştır. Bu çalışmada, Türkiye’nin 2009- 2019 yılları arasındaki kümülatif güneş enerjisi kurulu gücü verileri kullanılmıştır. Bu veriler ile 2020 yılı için kümülatif kurulu gücü tahmin etmek amacıyla Yapay Sinir Ağı (Artificial Neural Network - ANN) ve İki Yönlü Uzun-Kısa Vadeli Bellek (Bidirectional Long Short-Term Memory - BLSTM) yöntemleri kullanılmıştır. Kümülatif kurulu güç tahmin edilmiş ve sonuçlar karşılaştırılarak yorumlanmıştır.

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Cite This Article

İNCE, M., AYLAK, B. L., ÖZDEMİR, M. H., ORAL, O. (2022). INSTALLED SOLAR POWER PREDICTION FOR TURKEY USING ARTIFICIAL NEURAL NETWORK AND BIDIRECTIONAL LONG SHORT-TERM MEMORY. Business and Management Studies: An International Journal, Cilt: 8 - Sayı: 5, 4047–4068. https://ajindex.com/articles/649246

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References (11)

  1. Ahmed, R., El Sayed, M., Gadsden, S. A., Tjong, J., and Habibi, S. (2014). Automotive internalcombustion- engine fault detection and classification using artificial neural network techniques. IEEE Transactions on vehicular technology, 64(1), 21-33.
  2. Altınsoy, M., and Bal, G. (2019). Uzun dönem rüzgar hızı tahmininde yapay sinir ağlarının kullanımı ve performans incelemesi. Mesleki Bilimler Dergisi (MBD), 8(1), 21-28.
  3. Altuntop, N., and Erdemir, D. (2013). Dünyada ve Türkiye’de Güneş Enerjisi ile İlgili Gelişmeler. Mühendis ve Makine, 54(639), 69-77.
  4. Ata, R. (2008). Otonom Bir Rüzgâr Türbininin Farklı Yüksekliklerdeki Enerji Eldesini YSA ile Analizi. Journal of the Faculty of Engineering & Architecture of Gazi University, 23(3), 523-529.
  5. Bengio, Y., Simard, P., and Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks, 5(2), 157-166.
  6. Bilgili, M., Sahin, B., and Yasar, A. (2007). Application of artificial neural networks for the wind speed prediction of target station using reference stations data. Renewable Energy, 32, 2350–2360.
  7. Bin, Y., Yang, Y., Shen, F., Xie, N., Shen, H. T., and Li, X. (2018). Describing video with attention-based bidirectional LSTM. IEEE transactions on cybernetics, 49(7), 2631-2641.
  8. Bosch, J. L., Lopez, G., and Batlles, F. J. (2008). Daily solar irradiation estimation over a mountainous area using artificial neural network. Renewable Energy, 33, 1622–1628. BP British Petrol (2020a). Full Report - BP Statistical Review of World Energy 2020, https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energyeconomics/ statistical-review/bp-stats-review-2020-full-report.pdf (01.09.2020). BP British Petrol (2020b). Renewable Energy - BP Statistical Review of World Energy 2020, https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energyeconomics/ statistical-review/bp-stats-review-2020-renewable-energy.pdf (01.09.2020).
  9. Byvatov, E., Fechner, U., Sadowski, J., and Schneider, G. (2003). Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. Journal of chemical information and computer sciences, 43(6), 1882-1889.
  10. Cantürk, S. (2018). Bir rüzgar çiftliğinden yapay sinir ağlarıyla kısa süreli elektrik üretim tahmini. Hacettepe University Graduate School of Science and Engineering, Master’s Thesis, Ankara.
  11. Ceylan, R., and Başer, S. (2014). Türkiye’de Petrol Tüketimi İle Reel GSYİH Arasındaki