LSTM-CNN Tabanlı Derin Öğrenme Tekniği Kullanılarak Küresel Yatay Güneş Radyasyonu ile Hava Durumu Parametrelerinin Tahmini ve Analizi

Year 2022, , 340 - 356, 30.06.2022

Sercan Yalçın , Münür Sacit Herdem

https://doi.org/10.35193/bseufbd.1037563

Abstract

Önemli iklim parametrelerinin yanı sıra küresel yatay güneş ışınımının (GHSI) tahmin edilmesi, fotovoltaik panellerin enerji yönetimi ve kaynak planlamasında önemli bir rol oynamaktadır. Güneş enerjisinden daha fazla yararlanmak için, bu tür zaman serisi parametre verilerinin sıklıkla analiz edilmesi ve tahmin edilmesi yoluyla gelecek değerler hakkında bilgi elde edilmesi gerekmektedir. Bu nedenle, uzun vadeli güneş ışınımı verilerini tahmin etmek zorlu bir iştir. Bu amaçlarla, bu çalışmada, bu tür verilerin en doğru tahminini sağlamak için Uzun Kısa Süreli Bellek (LSTM) ve Evrişimsel Sinir Ağı (CNN) derin sinir ağlarının modellenmesi ile hibrit bir yöntem önerilmiştir. Ürdün vadisinde elde edilen GHSI ve sıcaklık, bağıl nem ve rüzgâr hızı verileri tahmin metodolojisinde kullanılır. Önerilen derin mimarinin CNN bloğunda, giriş parametreleri evrişim, havuzlama ve düzleştirme katmanlarından geçirilir ve çıkışlar LSTM veri girişine iletilir. Bu yöntemle daha etkin ve doğru tahminler yapılması hedeflenmektedir. Önerilen yöntem, diğer yöntemlerden farkını ortaya çıkarmak için RMSE, MADE ve MAPE hata performans kriterlerine göre karşılaştırılmıştır. Önerilen yöntem, özellikle GHSI tahmininde diğer algoritmalara göre daha üstün sonuçlar vermektedir.

Keywords

Küresel Güneş Işınımı, Derin Sinir Ağları, Uzun Kısa Süreli Bellek, Evrişimli Sinir Ağı

Prediction and Analysis of Weather Parameters with Global Horizontal Solar Irradiance Using LSTM-CNN Based Deep Learning Technique

Abstract

Predicting global horizontal solar irradiance (GHSI) as well as important climate parameters plays an important role in energy management and resource planning of photovoltaic panels. To further benefit from solar energy, it is necessary to obtain information regarding future values by frequently analyzing and predicting such time series parameter data. Hence, predicting long-term solar irradiance data is a challenging task. For these purposes, in this work, a hybrid method, with modeling of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) deep neural networks, is proposed to ensure the most accurate prediction of such data. The GHSI as well as temperature, relative humidity, and wind speed data obtained in the Jordan valley are used in the forecasting methodology. In the CNN block of the proposed deep architecture, the input parameters are passed through the convolution, pooling, and flattening layers, and the outputs are forwarded to the LSTM data input. With this method, it is aimed to make more effective and accurate estimations. The proposed method has been compared according to Root Mean Square Error (RMSE), Mean Absolute Deviation Error (MADE), and Mean Absolute Percentage Error (MAPE) error performance criteria in order to reveal the difference from other methods. The proposed method produces superior results compared to other algorithms, especially in GHSI estimation.

Keywords

Global Solar Irradiance, Deep Neural Networks, Long Short Term Memory, Convolutional Neural Network

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