Reconciling Food and Energy on the Same Land: An Explainable Artificial Intelligence Framework for Agrivoltaic Suitability Mapping

Öz

The swift growth of utility-scale solar farms has created fierce competition for agricultural land, especially in countries where prime solar energy sites and productive croplands coincide. Agrivoltaic structures, which incorporate agricultural food and electricity generation on one parcel, are a structural solution to this kind of land-use conflict; yet the question of where such systems should be sited has, to date, been addressed in the literature predominantly through subjective multi-criteria decision making methods. This study develops a fully data-driven and explainable machine learning framework for agrivoltaic site selection, using the province of Konya—Türkiye's grain basket and its largest agricultural region—as a case study. The site selection problem is formulated as a supervised binary classification task in which the geographic inventory of existing solar installations provides the ground-truth labels. Thirteen selected feature spanning climatic, topographic, economic, and socio-environmental dimensions were examined through Pearson correlation, variance inflation factor, and mutual information gain analyses. Logistic Regression, Random Forest, and Extreme Gradient Boosting machine learning algorithms were trained with hyperparameter optimization, and observed performance variations were verified using omnibus Cochran's Q and pairwise McNemar statistical tests. Extreme Gradient Boosting achieved the highest performance (total accuracy: 0.9549, F1 score: 0.9533), followed by Random Forest (0.9390) and Logistic Regression (0.8661). Explainable artificial intelligence analysis, which renders the black-box nature of the models transparent, revealed that the most decisive factors determining agrivoltaic suitability were, respectively, distance to grid lines and global horizontal irradiance (GHI). The findings suggest that the proposed methodology may help minimize conflicts between energy and food security, thereby providing a strategic planning tool that can support progress toward the Sustainable Development Goals (particularly SDGs 7 and 13).

Anahtar Kelimeler

• Agrivoltaics
• Machine learning
• Site selection
• Explainable artificial intelligence
• multi-criteria decision making

Etik Beyan

Ethics committee approval was not required for this study. Additionally, “The author declares no conflict of interest.

Teşekkür

The editor and the anonymous reviewers are gratefully acknowledged for their valuable comments and suggestions, which have considerably improved the quality of this study. Sincere thanks are also due to Atakan Bilgili and Tümay Arda for sharing their valuable insights on the subject of this research.

Gıda ve Enerjiyi Aynı Arazide Uzlaştırmak: Agrivoltaik Uygunluk Haritalaması için Açıklanabilir Yapay Zekâ Çerçevesi

Öz

Endüstriyel ölçekli güneş enerjisi santrallerinin hızlı büyümesi, özellikle güneş enerjisi için en uygun alanlarla verimli tarım arazilerinin çakıştığı ülkelerde, tarım arazileri için şiddetli bir rekabet ortamı yaratmıştır. Tek bir parselde gıda üretimi ile elektrik üretimini bir araya getiren agrivoltaik yapılar, bu tür arazi kullanımı çatışmalarına yönelik yapısal bir çözüm sunmakta; ancak agrivoltaik sistemlerin nerede kurulacağı sorusu literatürde bugüne kadar ağırlıklı olarak öznel çok kriterli karar verme yöntemleriyle ele alınmıştır. Bu çalışma, Türkiye'nin tahıl ambarı ve en büyük tarım bölgesi olan Konya ili örneğinde, agrivoltaik yer seçimi için tamamen veriye dayalı ve açıklanabilir bir makine öğrenmesi çerçevesi geliştirmektedir. Yer seçimi problemi, mevcut güneş enerjisi kurulumlarının coğrafi envanterinin gerçek referans etiketleri sağladığı denetimli bir ikili sınıflandırma görevi olarak formüle edilmiştir. İklimsel, topografik, ekonomik ve sosyo-çevresel boyutları kapsayan on üç adet nitelikli kriter; Pearson korelasyonu, varyans şişirme faktörü ve karşılıklı bilgi kazanımı yöntemleriyle incelenmiştir. Lojistik Regresyon, Rastgele Orman ve Aşırı Gradyan Artırma makine öğrenme modelleri hiperparametre optimizasyonuyla eğitilmiş, gözlemlenen performans farkları Cochran Q ve McNemar istatistiksel testleriyle doğrulanmıştır. En yüksek başarımı XGBoost (toplam doğruluk: 0.9549, F1 skor: 0.9533) göstermiş; onu Rastgele Orman (0.9390) ve Lojistik Regresyon (0.8661) izlemiştir. Modellerin kara kutu doğasını şeffaflaştıran açıklanabilir yapay zekâ analizi, agrivoltaik uygunluğu ortaya koyan en belirleyici faktörlerin sırasıyla şebeke hatlarına erişim mesafesi ve küresel yatay ışınım kapasitesi olduğunu göstermiştir. Elde edilen bulgular ışığında uygunluk analizleri, önerilen metodolojinin enerji ve gıda güvenliği çatışmalarını en aza indirgeyeceğini kanıtlamakta, böylelikle Sürdürülebilir Kalkınma Amaçlarına (özellikle 7 ve 13) son derece nesnel, şeffaf, güvenilir ve aktarılabilir bir stratejik planlama aracı kazandırmaktadır.

Anahtar Kelimeler

• Agrivoltaik
• Makine öğrenmesi
• Yer seçimi
• Açıklanabilir yapay zekâ
• Çok kriterli karar verme

Etik Beyan

Bu çalışma için etik kurul onayı gerekmemiştir. Ayrıca, yazar herhangi bir çıkar çatışması bulunmadığını beyan etmektedir.

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