Comparative Analysis of Empirical and AI-Supported Models in Global Solar Radiation Prediction for İzmir Province

Year 2025, Volume: 39 Issue: 1, 108 - 120

Ahmet Süslü

Abstract

Bu çalışmada, İzmir ili için küresel güneş radyasyonu tahmini yapabilecek farklı modellerin performansları karşılaştırmalı olarak analiz edilmiştir. ATATEK-Solar yazılımı kullanılarak, literatürde yaygın olarak kullanılan 14 ampirik model ve yeni geliştirilen bir yapay zeka destekli model test edilmiştir. Her model, üç farklı optimizasyon algoritması (Nelder-Mead Simplex, Pattern Search, Simulated Annealing) kullanılarak analiz edilmiştir. Çalışmada, Meteoroloji Genel Müdürlüğü’nden elde edilen uzun dönemli ortalama meteorolojik veriler kullanılmıştır. Analiz sonuçlarına göre, Model 15, RMSE: 0.1451 ve R²: 0.9995 değerleri ile en başarılı tahminleri gerçekleştirmiştir. Bunu, RMSE: 0.2016 ve R²: 0.9990 değerleriyle Model 5 ve RMSE: 0.2017 ve R²: 0.9990 değerleriyle Model 6 takip etmiştir. Modellerin aylık bazdaki performansları incelendiğinde, en düşük tahmin hatalarının ilkbahar ve yaz aylarında gerçekleştiği gözlemlenmiştir. Çalışmanın sonucunda, İzmir ilinin güneş enerjisi potansiyelinin değerlendirilmesi ve sistem tasarımı için Model 15’in kullanılması önerilmektedir.

Keywords

Güneş enerjisi, Küresel güneş radyasyonu, Ampirik modeller, Yapay zeka, İzmir

Comparative Analysis of Empirical and AI-Supported Models in Global Solar Radiation Prediction for İzmir Province

Abstract

In this study, the performances of different models that can be used to predict global solar radiation for İzmir province were analyzed comparatively. Using ATATEK-Solar software, 14 empirical models commonly used in the literature and a newly developed AI-supported model were tested. Each model was analyzed using three different optimization algorithms (Nelder-Mead Simplex, Pattern Search, Simulated Annealing). Long-term average meteorological data obtained from Turkish State Meteorological Service were used. According to the analysis results, Model 15 performed the most successful predictions with RMSE:0.1451 and R²:0.9995 values. This was followed by Model 5 with RMSE:0.2016 and R²:0.9990 values and Model 6 with RMSE:0.2017 and R²:0.9990 values. When model performances were examined on a monthly basis, it was observed that the lowest prediction errors occurred in spring and summer months. As a result of the study, it is recommended to use Model 15 in evaluating the solar energy potential of İzmir province and system design.

Keywords

Solar energy, Global solar radiation, Empirical models, Artificial intelligence, İzmir

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