müh.bil.ve araş.dergisi

Mühendislik Bilimleri ve Araştırmaları Dergisi

2687-4415

Bandırma Onyedi Eylül Üniversitesi

10.46387/bjesr.1786127

Photovoltaic Power Systems

Fotovoltaik Güç Sistemleri

LSTM-Transformer-Seq2Seq Hibrit Yaklaşımıyla Geliştirilmiş Kısa Vadeli PV Güç Tahmini

Short term PV Power Forecasting Enhanced with an LSTM-Transformer-Seq2Seq Hybrid Approach

https://orcid.org/0000-0001-7335-5185

Taşdemir

Bahtiyar

YOZGAT BOZOK ÜNİVERSİTESİ

04 30 2026

8 1 1 11 09 17 2025 11 12 2025

2019

Mühendislik Bilimleri ve Araştırmaları Dergisi

Fotovoltaik (PV) enerji üretimi, hava koşullarına bağlı olarak değişkenlik gösterdiğinden doğrusal olmayan ve karmaşık bir yapıya sahiptir. Bu durum güvenilir tahmin yöntemlerine olan ihtiyacı artırmaktadır. Bu çalışmada, fotovoltaik üretiminin daha doğru tahmin edilmesini sağlamak için Uzun Kısa Dönem Bellek (LSTM), Transformatör ve Seq2Seq modellerini birleştiren hibrit bir derin öğrenme modeli önerilmiştir. Önerilen modelde, geçmiş fotovoltaik üretim verileri uzun kısa dönem bellek tabanlı bir kodlayıcı kullanılarak işlenirken, meteorolojik veriler bir Transformer kodlayıcı kullanılarak analiz edilmiştir. Elde edilen özellik vektörleri birleştirildi ve gelecek tahminleri oluşturmak için Seq2Seq tabanlı bir kod çözücü yapısına beslendi. Model performansı Ortalama Mutlak Hata (MAE), Ortalama Kare Hatanın Kökü (RMSE) ve Ortalama Mutlak Yüzde Hata (MAPE) ölçütleri kullanılarak değerlendirilmiş ve sırasıyla 18.9 kW, 217.7 kW ve %9.36 değerleri elde edilmiştir. Sonuçlar, geliştirilen hibrit modelin fotovoltaik enerji tahmininde yüksek doğruluk sağladığını ve mevcut yöntemlere kıyasla üstün performans sergilediğini göstermektedir.

Photovoltaic (PV) energy production has a non-linear and complex structure, as it varies depending on weather conditions. This situation increases the need for reliable prediction methods. In this study, a composite deep learning approach that integrates Long Short-Term Memory (LSTM), Transformer, and Seq2Seq models has been proposed to enable more accurate forecasting of photovoltaic production. In the proposed model, historical photovoltaic production data was processed using a long short-term memory-based encoder, while meteorological data was analyzed using a Transformer encoder. The resulting feature vectors were combined and fed into a Seq2Seq based decoder structure to generate future predictions. Model performance was evaluated using the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics, yielding values of 18.9 kW, 217.7 kW, and 9.36%, respectively. It was observed that the hybrid model offers reliable accuracy in photovoltaic energy forecasting and performs better than previously used models.

Forecast Hybrid Photovoltaic power Renewable energy

Tahmin Hibrit Fotovoltaik güç Yenilenebilir enerji

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