Estimating Solar Energy within the scope of environmental factors by the Neural Network algorithm

Yıl 2024, , 24 - 34, 30.04.2024

Yasemin Ayaz Atalan

https://doi.org/10.46387/bjesr.1377273

Öz

The efficiency of solar energy systems requires a complicated forecasting process due to the variability of sunlight and environmental conditions. Among environmental factors, cloud coverage (% range), temperature (0C), wind speed (Mph), and humidity (%) variables were taken into account in this study. Neural networks (NN), which are machine learning (ML) algorithms with a flexible structure that can define complex relationships and process large amounts of data for solar energy prediction, were used in this study. The NN algorithm showed a high performance, with mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2) values calculated as 0.019, 0.139, 0.053, and 0.977, respectively. This study emphasized that solar energy predictions made with the NN algorithm, considering environmental factors, are an essential tool that helps use solar energy systems more efficiently and sustainably.

Anahtar Kelimeler

Machine Learning, Neural Network Algorithm, Solar Energy, Environmental Factors

Sinir ağı algoritması ile çevresel faktörler kapsamında Güneş Enerjisinin tahmin edilmesi

Öz

Güneş enerjisi sistemlerinin verimliliği, güneş ışığının ve çevre koşullarının değişkenliği nedeniyle karmaşık bir tahmin süreci gerektirmektedir. Bu çalışmada çevresel faktörlerden bulut kapsamı (% aralık), hava sıcaklığı (0C), rüzgar hızı (Mph) ve bağıl nem (%) değişkenleri dikkate alınmıştır. Bu çalışmada güneş enerjisi tahmini için karmaşık ilişkileri tanımlayabilen ve büyük miktarda veriyi işleyebilen esnek yapıya sahip makine öğrenmesi (ML) algoritmalarından sinir ağı (NN) kullanılmıştır. NN algoritması, ortalama kare hatası (MSE), kök ortalama kare hatası (RMSE), ortalama mutlak hatası (MAE), ve R-kare (R2) değerleri sırasıyla 0,019, 0,139 0,053 ve 0,977 olarak hesaplanarak yüksek bir performans göstermiştir. Bu çalışmada, çevresel faktörler dikkate alınarak NN algoritması ile yapılan güneş enerjisi tahminlerinin, güneş enerjisi sistemlerinin daha verimli ve sürdürülebilir kullanılmasına yardımcı olan önemli bir araç olduğu vurgulanmıştır.

Anahtar Kelimeler

Makine Öğrenimi, Sinir Ağı Algoritması, Güneş Enerjisi, Çevresel Faktörler

Kaynakça

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