SOLAR IRRADIANCE PREDICTION USING BAGGING DECISION TREE-BASED MACHINE LEARNING

Yıl 2022, , 15 - 24, 30.06.2022

Hayrettin Toylan

https://doi.org/10.34186/klujes.1106357

Cited By: 1

Öz

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.

Anahtar Kelimeler

Renewable energy, Machine learning, Bagging decision tree, Solar irradiance prediction

TORBALAMA KARAR AĞACI TABANLI MAKINE ÖĞRENIMI KULLANARAK GÜNEŞ IŞINIMI TAHMİNİ

Öz

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.

Anahtar Kelimeler

Yenilenebilir enerji, Makine öğrenmesi, Torbalama karar ağacı, Güneş ışınımı tahmini

Kaynakça

• Akarslan, E., & Hocaoglu, F. O. A novel method based on similarity for hourly solar irradiance forecasting. Renewable Energy, 112, 337-346, 2017.
• Kamadinata, J. O., Ken, T. L., & Suwa, T. Sky image-based solar irradiance prediction methodologies using artificial neural networks. Renewable Energy, 134, 837-845, 2019.
• Dong, N., Chang, J. F., Wu, A. G., & Gao, Z. K. A novel convolutional neural network framework based solar irradiance prediction method. International Journal of Electrical Power & Energy Systems, 114, 105411, 2020.
• Tasnin, W., & Saikia, L. C. Deregulated AGC of multi-area system incorporating dish-Stirling solar thermal and geothermal power plants using fractional order cascade controller. International Journal of Electrical Power & Energy Systems, 101, 60-74, 2018.
• Almonacid, F., Pérez-Higueras, P. J., Fernández, E. F., & Hontoria, L. A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator. Energy Conversion and Management, 85, 389-398, 2014
• Gutierrez-Corea, F. V., Manso-Callejo, M. A., Moreno-Regidor, M. P., & Manrique-Sancho, M. T. Forecasting short-term solar irradiance based on artificial neural networks and data from neighboring meteorological stations. Solar Energy, 134, 119-131, 2016.
• Aljanad, A., Tan, N. M., Agelidis, V. G., & Shareef, H. Neural network approach for global solar irradiance prediction at extremely short-time-intervals using particle swarm optimization algorithm. Energies, 14(4), 1213, 2021.
• Feng, Y., Gong, D., Zhang, Q., Jiang, S., Zhao, L., & Cui, N. Evaluation of temperature-based machine learning and empirical models for predicting daily global solar radiation. Energy Conversion and Management, 198, 111780, 2019.
• Rai, A., Shrivastava, A., & Jana, K. C. A CNN‐BiLSTM based deep learning model for mid‐term solar radiation prediction. International Transactions on Electrical Energy Systems, 31(9), e12664, 2021.
• Aslam, M., Lee, J. M., Kim, H. S., Lee, S. J., & Hong, S. Deep learning models for long-term solar radiation forecasting considering microgrid installation: A comparative study. Energies, 13(1), 147, 2019.
• Lee, J., Wang, W., Harrou, F., & Sun, Y. Reliable solar irradiance prediction using ensemble learning-based models: A comparative study. Energy Conversion and Management, 208, 112582, 2020.
• Xu, M., Watanachaturaporn, P., Varshney, P. K., & Arora, M. K. Decision tree regression for soft classification of remote sensing data. Remote Sensing of Environment, 97(3), 322-336, 2005.
• Lu, H., & Ma, X. Hybrid decision tree-based machine learning models for short-term water quality prediction. Chemosphere, 249, 126169, 2020.
• Breiman, L. Bagging predictors. Machine learning, 24(2), 123-140, 1996.
• Buhlmann P, Yu B. Analyzing bagging. Ann Stat 30:927–61, 2002.
• Prasad, A. M., Iverson, L. R., & Liaw, A. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems, 9(2), 181-199, 2006.
• Kovačević, M., Ivanišević, N., Petronijević, P., & Despotović, V. Construction cost estimation of reinforced and prestressed concrete bridges using machine learning. Građevinar, 73(01.), 1-13, 2021.
• Harrou, F., Saidi, A., & Sun, Y. Wind power prediction using bootstrap aggregating trees approach to enabling sustainable wind power integration in a smart grid. Energy Conversion and Management, 201, 112077, 2019.