gummfd

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

1300-1884 1304-4915

Gazi Üniversitesi

10.17341/gazimmfd.1533969

Deep Learning Photovoltaic Power Systems

Derin Öğrenme Fotovoltaik Güç Sistemleri

Çok girdili-çok değişkenli çıktıya sahip derin öğrenme modelleri ile kısa vadeli güneş ışınım şiddeti ve sıcaklık tahmini

Short-term forecasting of solar irradiance and temperature using deep learning models with multiple inputs and multiple outputs

https://orcid.org/0000-0003-3983-2608

Kaysal

Kübra

AFYON KOCATEPE ÜNİVERSİTESİ

https://orcid.org/0000-0002-3640-7676

Hocaoğlu

Fatih Onur

AFYON KOCATEPE ÜNİVERSİTESİ

https://orcid.org/0000-0002-0607-1868

Ozturk

Nihat

GAZİ ÜNİVERSİTESİ

03 31 2026

41 1 463 478 08 15 2024 12 28 2025

1986

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

Güneş ışınımının kesikli ve dalgalı yapısı çoğu uygulama için ciddi sınırlamalar oluşturur. Güneş ışınım şiddetinin doğru tahmini, bir fotovoltaik güç sisteminin çıkış gücünün tahmin edilmesinde önemli bir faktördür. Bu çalışmada, kısa dönemli tahminler için çok değişkenli girdilerin iki değişkenli çıktılara etkisi incelenmiş ve bir bölgeye kurulması planlanan güneş enerji santrali için meteorolojik değişimlerin etkileri araştırılmıştır. Ayrıca çeşitli derin öğrenme modelleri ve onların hibrit kombinasyonlarının güneş ışınım şiddeti ve sıcaklık tahmini için başarıları kıyaslanmıştır. M/CNN-BİLSTM_II modeli diğer modellere kıyasla üç girdi parametresi sıcaklık, ışınım şiddeti ve nem için hem sıcaklık hem de ışınım şiddeti tahmininde en iyi performansı sergilemiştir. Modellerin performansı için RMSE, MAE, NRMSE ve R2 metrikleri kullanılmıştır. Işınım şiddeti için bu metrikler sırasıyla 1,65 W/m² (RMSE), 35,7 W/m² (MAE), %6,71 (NRMSE) ve %94,61 (R²) olarak hesaplanmıştır. Sıcaklık değerleri için ise RMSE 0,79°C, MAE 0,58°C, NRMSE %1,68 ve R² %99,32 olarak elde edilmiştir.

The intermittent and fluctuating nature of solar radiation poses significant limitations for many applications. Accurate estimation of solar radiation is a crucial factor in predicting the output power of a photovoltaic system. In this study, the effects of multivariate inputs on bivariate outputs for short-term forecasts were examined, and the impact of meteorological changes on a solar power plant planned for a specific region was investigated. Additionally, the performance of various deep learning models and their hybrid combinations for predicting solar radiation and temperature was compared. Compared to other models, the M/CNN-BİLSTM_II model demonstrated the best performance in estimating both temperature and radiation intensity using the three input parameters: temperature, solar radiation, and humidity. The performance of the models was evaluated using RMSE, MAE, NRMSE, and R² metrics. For solar radiation, these metrics were calculated as 1.65 W/m² (RMSE), 35.7 W/m² (MAE), 6.71% (NRMSE), and 94.61% (R²), respectively. For temperature values, RMSE was obtained as 0.79°C, MAE as 0.58°C, NRMSE as 1.68%, and R² as 99.32%.

Işınım şiddeti tahmini sıcaklık tahmini derin öğrenme çift yönlü uzun- kısa süreli bellek çok girdili-çok değişkenli çıkış

Solar radiation estimation temperature estimation deep learning bidirectional long-short term memory multi-input-multivariate output models.

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