Kirklareli University Journal of Engineering and Science

2458-7494 2458-7613

Kirklareli University

10.34186/klujes.1106357

Engineering

Mühendislik

SOLAR IRRADIANCE PREDICTION USING BAGGING DECISION TREE-BASED MACHINE LEARNING

TORBALAMA KARAR AĞACI TABANLI MAKINE ÖĞRENIMI KULLANARAK GÜNEŞ IŞINIMI TAHMİNİ

https://orcid.org/0000-0001-8542-7254

Toylan

Hayrettin

KIRKLARELI UNIVERSITY

06 30 2022

8 1 15 24 04 20 2022 06 30 2022

2015

Kirklareli University Journal of Engineering and Science

Solar energy is one of the most widely used renewable energy sources to generate electricity. However, the amount of solar radiation reaching the earth's surface is variable, creating uncertainty in the output of electrical power generation systems that use this source. Therefore, solar irradiance prediction becomes a critical process in planning. This study presents a short-term prediction of solar irradiance using bagging decision tree-based machine learning. As the inputs of the proposed method, air temperature, hour, day, month, and previous solar irradiance values were determined. The performance of the proposed method is tested on the measured data. The R2 and RMSE values are 0.87 and 91.282, respectively, according to the results obtained. As a result, it has been revealed that the varying solar irradiance can be predicted with acceptable differences with this method.

Yenilenebilir enerji kaynaklarından biri olan güneş ışınımlarının dünya yüzeyine düşen miktarının değişken olması bu kaynağı kullanan özellikle elektrik güç üretim sistemlerinin çıktısında belirsizlik yaratır. Bu nedenle güneş ışınımı tahmini planlamada çok önemli bir süreç haline gelmektedir. Bu makale, torbalama karar ağacı tabanlı makine öğrenimini kullanarak güneş ışınımının kısa vadeli bir tahminini elde etmeyi amaçlamaktadır. Önerilen yöntemin girdileri olarak hava sıcaklığı, saat, gün, ay ve önceki güneş ışınım değeri belirlenmiştir. Yöntemin performansı ölçülen veriler üzerinde test edilmiştir. Elde edilen sonuçlara göre R2 ve RMSE değeri sırasıyla 0.87 ve 91.282 olarak bulunmuştur. Sonuç olarak bu yöntem ile değişen güneş ışınımlarının kabul edilebilir farklılıklarla tahmin edilebilir olduğu ortaya konmuştur.

Renewable energy Machine learning Bagging decision tree Solar irradiance prediction

Yenilenebilir enerji Makine öğrenmesi Torbalama karar ağacı Güneş ışınımı tahmini

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