EN
TR
SOLAR IRRADIANCE PREDICTION USING BAGGING DECISION TREE-BASED MACHINE LEARNING
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
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.
Ayrıntılar
Birincil Dil
İngilizce
Konular
Mühendislik
Bölüm
Araştırma Makalesi
Yazarlar
Yayımlanma Tarihi
30 Haziran 2022
Gönderilme Tarihi
20 Nisan 2022
Kabul Tarihi
30 Haziran 2022
Yayımlandığı Sayı
Yıl 2022 Cilt: 8 Sayı: 1
APA
Toylan, H. (2022). SOLAR IRRADIANCE PREDICTION USING BAGGING DECISION TREE-BASED MACHINE LEARNING. Kirklareli University Journal of Engineering and Science, 8(1), 15-24. https://doi.org/10.34186/klujes.1106357
AMA
1.Toylan H. SOLAR IRRADIANCE PREDICTION USING BAGGING DECISION TREE-BASED MACHINE LEARNING. KLUJES. 2022;8(1):15-24. doi:10.34186/klujes.1106357
Chicago
Toylan, Hayrettin. 2022. “SOLAR IRRADIANCE PREDICTION USING BAGGING DECISION TREE-BASED MACHINE LEARNING”. Kirklareli University Journal of Engineering and Science 8 (1): 15-24. https://doi.org/10.34186/klujes.1106357.
EndNote
Toylan H (01 Haziran 2022) SOLAR IRRADIANCE PREDICTION USING BAGGING DECISION TREE-BASED MACHINE LEARNING. Kirklareli University Journal of Engineering and Science 8 1 15–24.
IEEE
[1]H. Toylan, “SOLAR IRRADIANCE PREDICTION USING BAGGING DECISION TREE-BASED MACHINE LEARNING”, KLUJES, c. 8, sy 1, ss. 15–24, Haz. 2022, doi: 10.34186/klujes.1106357.
ISNAD
Toylan, Hayrettin. “SOLAR IRRADIANCE PREDICTION USING BAGGING DECISION TREE-BASED MACHINE LEARNING”. Kirklareli University Journal of Engineering and Science 8/1 (01 Haziran 2022): 15-24. https://doi.org/10.34186/klujes.1106357.
JAMA
1.Toylan H. SOLAR IRRADIANCE PREDICTION USING BAGGING DECISION TREE-BASED MACHINE LEARNING. KLUJES. 2022;8:15–24.
MLA
Toylan, Hayrettin. “SOLAR IRRADIANCE PREDICTION USING BAGGING DECISION TREE-BASED MACHINE LEARNING”. Kirklareli University Journal of Engineering and Science, c. 8, sy 1, Haziran 2022, ss. 15-24, doi:10.34186/klujes.1106357.
Vancouver
1.Hayrettin Toylan. SOLAR IRRADIANCE PREDICTION USING BAGGING DECISION TREE-BASED MACHINE LEARNING. KLUJES. 01 Haziran 2022;8(1):15-24. doi:10.34186/klujes.1106357
Cited By
Performance evaluation of seasonal solar irradiation models—case study: Karapınar town, Turkey
Case Studies in Thermal Engineering
https://doi.org/10.1016/j.csite.2023.103228Machine learning-based prediction of solar radiation in the Southeastern Anatolia Region of Türkiye
International Journal of Energy Studies
https://doi.org/10.58559/ijes.1633454