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A Techno-Economic Analysis of Power Generation in Wind Power Plants Through Deep Learning: A Case Study of Türkiye

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  • Ziya Demirkol

    (Department of Electrical, Vocational of School, Kastamonu University, Kastamonu 37160, Türkiye)

  • Faruk Dayi

    (Department of Business Administration, Faculty of Economics and Administrative Sciences, Kastamonu University, Kastamonu 37160, Türkiye)

  • Aylin Erdoğdu

    (Department of Finance and Banking, Faculty of Economics and Administrative Sciences, Istanbul Arel University, Istanbul 34295, Türkiye)

  • Ahmet Yanik

    (Department of Business Administration, Faculty of Economics and Administrative Sciences, Recep Tayyip Erdoğan University, Rize 53100, Türkiye)

  • Ayhan Benek

    (Department of Business Administration, Faculty of Economics and Administrative Sciences, Kastamonu University, Kastamonu 37160, Türkiye)

Abstract

In recent years, the utilization of renewable energy sources has significantly increased due to their environmentally friendly nature and sustainability. Among these sources, wind energy plays a critical role, and accurately forecasting wind power with minimal error is essential for optimizing the efficiency and profitability of wind power plants. This study analyzes hourly wind speed data from 23 meteorological stations located in Türkiye’s Western Black Sea Region for the years 2020–2024, using the Weibull distribution to estimate annual energy production. Additionally, the same data were forecasted using the Long Short-Term Memory (LSTM) model. The predicted data were also assessed through Weibull distribution analysis to evaluate the energy potential of each station. A comparative analysis was then conducted between the Weibull distribution results of the measured and forecast datasets. Based on the annual energy production estimates derived from both datasets, the revenues, costs, and profits of 10 MW wind farms at each location were examined. The findings indicate that the highest revenues and unit electricity profits were observed at the Zonguldak South, Sinop İnceburun, and Bartın South stations. According to the LSTM-based forecasts for 2025, investment in wind energy projects is considered feasible at the Sinop İnceburun, Bartın South, Zonguldak South, İnebolu, Cide North, Gebze Köşkburnu, and Amasra stations.

Suggested Citation

  • Ziya Demirkol & Faruk Dayi & Aylin Erdoğdu & Ahmet Yanik & Ayhan Benek, 2025. "A Techno-Economic Analysis of Power Generation in Wind Power Plants Through Deep Learning: A Case Study of Türkiye," Energies, MDPI, vol. 18(10), pages 1-40, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:10:p:2632-:d:1659689
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    as
    1. Shayan, Mostafa Esmaeili & Najafi, Gholamhassan & Ghobadian, Barat & Gorjian, Shiva & Mamat, Rizalman & Ghazali, Mohd Fairusham, 2022. "Multi-microgrid optimization and energy management under boost voltage converter with Markov prediction chain and dynamic decision algorithm," Renewable Energy, Elsevier, vol. 201(P2), pages 179-189.
    2. Hugo Díaz & Carlos Guedes Soares, 2021. "A Multi-Criteria Approach to Evaluate Floating Offshore Wind Farms Siting in the Canary Islands (Spain)," Energies, MDPI, vol. 14(4), pages 1-18, February.
    3. Xiangqian Li & Keke Li & Siqi Shen & Yaxin Tian, 2023. "Exploring Time Series Models for Wind Speed Forecasting: A Comparative Analysis," Energies, MDPI, vol. 16(23), pages 1-22, November.
    4. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    5. Mehr Gul & Nengling Tai & Wentao Huang & Muhammad Haroon Nadeem & Moduo Yu, 2019. "Assessment of Wind Power Potential and Economic Analysis at Hyderabad in Pakistan: Powering to Local Communities Using Wind Power," Sustainability, MDPI, vol. 11(5), pages 1-23, March.
    6. Alessio Terzi & Roger Fouquet, 2023. "The Green Industrial Revolution: Lessons from the History of Past Energy Transitions," EconPol Forum, CESifo, vol. 24(06), pages 16-22, November.
    7. de Prada Gil, Mikel & Gomis-Bellmunt, Oriol & Sumper, Andreas, 2014. "Technical and economic assessment of offshore wind power plants based on variable frequency operation of clusters with a single power converter," Applied Energy, Elsevier, vol. 125(C), pages 218-229.
    8. Sajid Ali & Sang-Moon Lee & Choon-Man Jang, 2017. "Techno-Economic Assessment of Wind Energy Potential at Three Locations in South Korea Using Long-Term Measured Wind Data," Energies, MDPI, vol. 10(9), pages 1-24, September.
    9. Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, July.
    10. Yingying He & Likai Zhang & Tengda Guan & Zheyu Zhang, 2024. "An Integrated CEEMDAN to Optimize Deep Long Short-Term Memory Model for Wind Speed Forecasting," Energies, MDPI, vol. 17(18), pages 1-29, September.
    11. Navid Shirzadi & Fuzhan Nasiri & Ramanunni Parakkal Menon & Pilar Monsalvete & Anton Kaifel & Ursula Eicker, 2023. "Smart Urban Wind Power Forecasting: Integrating Weibull Distribution, Recurrent Neural Networks, and Numerical Weather Prediction," Energies, MDPI, vol. 16(17), pages 1-17, August.
    12. Sandra Minerva Valdivia-Bautista & José Antonio Domínguez-Navarro & Marco Pérez-Cisneros & Carlos Jesahel Vega-Gómez & Beatriz Castillo-Téllez, 2023. "Artificial Intelligence in Wind Speed Forecasting: A Review," Energies, MDPI, vol. 16(5), pages 1-28, March.
    13. Shahram Hanifi & Xiaolei Liu & Zi Lin & Saeid Lotfian, 2020. "A Critical Review of Wind Power Forecasting Methods—Past, Present and Future," Energies, MDPI, vol. 13(15), pages 1-24, July.
    14. Baek, Jae-Seung & Kang, Jun-Koo & Suh Park, Kyung, 2004. "Corporate governance and firm value: evidence from the Korean financial crisis," Journal of Financial Economics, Elsevier, vol. 71(2), pages 265-313, February.
    15. Singla, Neetu & Jain, Kanchan & Kumar Sharma, Suresh, 2012. "The Beta Generalized Weibull distribution: Properties and applications," Reliability Engineering and System Safety, Elsevier, vol. 102(C), pages 5-15.
    16. Qiuhong Huang & Xiao Wang, 2022. "A Forecasting Model of Wind Power Based on IPSO–LSTM and Classified Fusion," Energies, MDPI, vol. 15(15), pages 1-19, July.
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