An Explainable Machine Learning Approach to Offshore Wind Farm Suitability Assessment: A GIS-Based Decision Support Framework for Türkiye

Esra Özkan Aksu; Cevriye Gencer

An Explainable Machine Learning Approach to Offshore Wind Farm Suitability Assessment: A GIS-Based Decision Support Framework for Türkiye

Abstract

Offshore wind energy has become a key part of the global renewable energy transition. In this study, a machine learning–based classification model was developed for Türkiye’s marine areas by using the suitability zones previously identified through Geographic Information Systems (GIS)–based Boolean analysis and Multi-Criteria Decision-Making (MCDM) methods. The main goal is to build an understandable decision support system (DSS) that can predict site suitability for new locations by modeling these existing suitability results with explainable machine learning techniques. For this purpose, the Random Forest classification algorithm was trained on approximately 4 million data points representing 24 technical, environmental, socio-economic, and security-related criteria. The model’s decision logic was interpreted using the SHAP (Shapley Additive Explanations) method. The results show that wind speed, water depth, and capacity factor are the most important variables in predicting offshore wind farm suitability. Besides validating previously identified suitable areas, the developed model acts as an explainable DSS with both predictive and interpretive abilities for unexplored sites. This combined approach—merging traditional GIS and MCDM results with machine learning—offers a more flexible, scalable, and transparent framework for assessing Türkiye’s offshore wind energy potential.

Keywords

Offshore wind energy , Site selection , GIS , Random Forest , SHAP , Explainable machine learning

References

[1] H. Díaz and C. Guedes Soares, "Review of the current status, technology and future trends of offshore wind farms," Ocean Engineering, vol. 209, 107381, Aug. 2020. doi: 10.1016/j.oceaneng.2020.107381
[2] Global Wind Energy Council (GWEC), "Global Offshore Wind Report 2024," Brussels, 2024. [Online]. Available: https://www.gwec.net/reports/globaloffshorewindreport/2024. [Accessed: Nov. 24, 2024].
[3] D. G. Vagiona and M. Kamilakis, "Sustainable site selection for offshore wind farms in the South Aegean—Greece," Sustainability, vol. 10, no. 3, 749, Mar. 2018. doi: 10.3390/su10030749
[4] A. Chaouachi, C. F. Covrig, and M. Ardelean, "Multi-criteria selection of offshore wind farms: Case study for Baltic States," Energy Policy, vol. 103, pp. 179-192, Apr. 2017. doi: 10.1016/j.enpol.2017.01.018
[5] P. Gkeka-Serpetsidaki, G. Skiniti, S. Tournaki, and T. Tsoutsos, "A review of the sustainable siting of offshore wind farms," Sustainability, vol. 16, no. 14, 6036, Jul. 2024. doi: 10.3390/su16146036
[6] I. C. Dimitriou, E. Sarmas, G. P. Trachanas, V. Marinakis, and H. Doukas, "Multi-criteria GIS-based offshore wind farm site selection: Case study in Greece," Renewable and Sustainable Energy Reviews, vol. 207, 114962, Jan. 2025. doi: 10.1016/j.rser.2024.114962
[7] Y. Wu, J. Zhang, J. Yuan, S. Geng, and H. Zhang, "Study of decision framework of offshore wind power station site selection based on ELECTRE-III under intuitionistic fuzzy environment: A case of China," Energy Conversion and Management, vol. 113, pp. 66-81, Apr. 2016. doi: 10.1016/j.enconman.2016.01.020
[8] C. Rudin, "Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead," Nature Machine Intelligence, vol. 1, no. 5, pp. 206-215, May 2019. doi: 10.1038/s42256-019-0048-x
[9] S. M. Lundberg and S.-I. Lee, "A unified approach to interpreting model predictions, " in Advances in Neural Information Processing Systems 30 (NIPS 2017), I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds. Curran Associates, Inc., 2017, pp. 4765-4774.
[10] V. Belle and I. Papantonis, "Principles and practice of explainable machine learning," Frontiers in Big Data, vol. 4, 688969, Jul. 2021. doi: 10.3389/fdata.2021.688969

[11] W. Liao, J. Fang, L. Ye, B. Bak-Jensen, Z. Yang, and F. Porté-Agel, "Can we trust explainable artificial intelligence in wind power forecasting?" Applied Energy, vol. 376, 124273, Dec. 2024. doi: 10.1016/j.apenergy.2024.124273
[12] E. Ö. Aksu and C. T. Gencer, "GIS-based optimum location selection for offshore wind energy: A case study for Turkey," Ocean Engineering, vol. 320, 120292, Mar. 2025. doi: 10.1016/j.oceaneng.2025.120292
[13] A. Yaman, "A GIS-based multi-criteria decision-making approach (GIS-MCDM) for determination of the most appropriate site selection of onshore wind farm in Adana, Turkey," Clean Technologies and Environmental Policy, vol. 26, no. 12, pp. 4231-4254, May. 2024. doi: 10.1007/s10098-024-02866-3
[14] A. Demir, A. E. Dinçer, C. Çiftçi, S. Gülçimen, N. Uzal, and K. Yılmaz, "Wind farm site selection using GIS-based multicriteria analysis with life cycle assessment integration," Earth Science Informatics, vol. 17, pp. 1591-1608, Jan. 2024. doi: 10.1007/s12145-024-01227-4
[15] A. Bilgili, T. Arda, and B. Kilic, "Explainability in wind farm planning: A machine learning framework for automatic site selection of wind farms," Energy Conversion and Management, vol. 309, 118441, Jun. 2024. doi: 10.1016/j.enconman.2024.118441
[16] J. Zhang, T. Zhang, Y. Li, X. Bai, and L. Chang, "Study on mining wind information for identifying potential offshore wind farms using deep learning," Frontiers in Energy Research, vol. 12, 1419549, Aug. 2024. doi: 10.3389/fenrg.2024.1419549 [17] M. Masoumi, "Machine learning solutions for offshore wind farms: A review of applications and impacts," Journal of Marine Science and Engineering, vol. 11, no. 10, 1855, Sep. 2023. doi: 10.3390/jmse11101855
[18] R. Machlev, L. Heistrene, M. Perl, K. Y. Levy, J. Belikov, S. Mannor, and Y. Levron, "Explainable Artificial Intelligence (XAI) techniques for energy and power systems: Review, challenges and opportunities," Energy and AI, vol. 9, 100169, Aug. 2022. doi: 10.1016/j.egyai.2022.100169
[19] A. M. Sazib, S. Al Farabi, F. Rayhan, and M. A. Karim, "Advancing renewable energy systems through explainable artificial intelligence: A comprehensive review and interdisciplinary framework," Journal of Computer Science and Technology Studies, vol. 7, no. 2, pp. 56-70, Apr. 2025. doi: 10.32996/jcsts.2025.7.2.5
[20] M. R. Shadi, H. Mirshekali, H. R. Shaker, "Explainable artificial intelligence for energy systems maintenance: A review on concepts, current techniques, challenges, and prospects," Renewable and Sustainable Energy Reviews, vol. 216, 115668, Jul. 2025. doi: 10.1016/j.rser.2025.115668
[21] T. Clement, H. T. T. Nguyen, N. Kemmerzell, M. Abdelaal, and D. Stjelja, "Coping with data distribution shifts: XAI-based adaptive learning with SHAP clustering for energy consumption prediction," in AI 2023: Advances in Artificial Intelligence, Lecture Notes in Computer Science, vol. 14472, T. Liu, G. Webb, L. Yue, and D. Wang, Eds. Singapore: Springer, 2024, pp. 147–159. doi: 10.1007/978-981-99-8391-9_12
[22] M. Argin, V. Yerci, N. Erdogan, S. Kucuksari, and U. Cali, "Exploring the offshore wind energy potential of Turkey based on multi-criteria site selection," Energy Strategy Reviews, vol. 23, pp. 33-46, Jan. 2019. doi: 10.1016/j.esr.2018.12.005
[23] E. Caceoğlu, H. K. Yildiz, E. Oğuz, N. Huvaj, and J. M. Guerrero, "Offshore wind power plant site selection using Analytical Hierarchy Process for Northwest Turkey," Ocean Engineering, vol. 252, 111178, May 2022. doi: 10.1016/j.oceaneng.2022.111178
[24] E. Ö. Aksu, "Türkiye deniz üstü rüzgâr enerjisi santralleri için Coğrafi Bilgi Sistemleri destekli Boolean ve ağırlıklı analiz yöntemleriyle yer seçimi," Ph.D. dissertation, Dept. of Industrial Eng., Gazi Univ., Ankara, Türkiye, 2025.
[25] World Bank, "Offshore Wind Roadmap for Türkiye," Washington, D.C., 2024. [Online]. Available: http://documents.worldbank.org/curated/en/099110624015521050. [Accessed: Oct. 3, 2025].
[26] L. Breiman, "Random forests," Machine Learning, vol. 45, no. 1, pp. 5-32, Oct. 2001. doi: 10.1023/A:1010933404324
[27] T. Hengl, M. Nussbaum, M. N. Wright, G. B. M. Heuvelink, and B. Gräler, "Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables," PeerJ, vol. 6, e5518, Aug. 2018. doi: 10.7717/peerj.5518
[28] S. Georganos, T. Grippa, A. Niang Gadiaga, C. Linard, M. Lennert, S. Vanhuysse, N. Mboga, E. Wolff, and S. Kalogirou, "Geographical random forests: A spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling," Geocarto International, vol. 36, no. 2, pp. 121-136, Jun. 2019. doi: 10.1080/10106049.2019.1595177
[29] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É. Duchesnay, "Scikit-learn: Machine learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825-2830, Oct. 2011. [Online]. Available: https://jmlr.org/papers/v12/pedregosa11a.html
[30] P. V. Sireesha and S. Thotakura, “Wind power prediction using optimized MLP-NN machine learning forecasting model,” Electrical Engineering, vol. 106, pp. 7643–7666, May. 2024. doi: 10.1007/s00202-024-02440-6
[31] Y. Sun, Y. Li, R. Wang, and R. Ma, “Modelling potential land suitability of large-scale wind energy development using explainable machine learning techniques: Applications for China, USA and EU,” Energy Conversion and Management, vol. 302, 118131, Feb. 2024. doi: 10.1016/j.enconman.2024.118131

Details

Primary Language

English

Subjects

Industrial Engineering

Journal Section

Research Article

Authors

Esra Özkan Aksu ^*
0000-0003-2142-2221
Türkiye

Cevriye Gencer
0000-0002-3373-8306
Türkiye

Publication Date

April 30, 2026

Submission Date

December 1, 2025

Acceptance Date

April 20, 2026

Published in Issue

Year 2026 Volume: 12 Number: 1

IZ

https://izlik.org/JA65LZ76EM

IEEE

[1]E. Özkan Aksu and C. Gencer, “An Explainable Machine Learning Approach to Offshore Wind Farm Suitability Assessment: A GIS-Based Decision Support Framework for Türkiye”, GJES, vol. 12, no. 1, pp. 75–95, Apr. 2026, [Online]. Available: https://izlik.org/JA65LZ76EM

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