THERMAL SCIENCE

International Scientific Journal

Thermal Science - Online First

Authors of this Paper

External Links

online first only

Global solar radiation prediction model with random forest algorithm

ABSTRACT
Global solar radiation estimation is crucial for regional climate assessment and crop growth. Therefore, studies on the prediction of solar radiation are emerging. With the availability of the public data on solar radiation, computerized models have been developed as well. These Predictive Models play significant role in determining the potentials of regions suitable for renewable energy generation required by engineering and agricultural activities. Herein a computerized model has been presented for estimating global solar radiation. The model utilizes random forest algorithm and reached predictive value of 93.9%.
KEYWORDS
PAPER SUBMITTED: 2020-06-08
PAPER REVISED: 2020-10-19
PAPER ACCEPTED: 2020-10-26
PUBLISHED ONLINE: 2021-01-24
DOI REFERENCE: https://doi.org/10.2298/TSCI200608004K
REFERENCES
  1. Kallioğlu, M. A. (2014). Niğde İli İçin Yatay Düzenleme Gelen Günlük Tüm. Yayılı ve Direkt Güneş Işınımını Hesaplama Modeli Geliştirilmesi (Doctoral dissertation, Niğde Üniversitesi).
  2. GÜRBÜZ, A. (2009). "Enerji Piyasası İçerisinde Yenilenebilir (Temiz) Enerji Kaynaklarının Yeri ve Önemi", 5. Uluslararası Đleri Teknolojiler Sempozyumu, 13-15 Mayıs, Karabük.
  3. Rıfkın J., Howard T., "Entropi Dünyaya Yeni Bir Bakış", İz Yayınevi, 1997
  4. Ültanır, M. Ö. (1996). Solar energy is on the verge of the century. Bilim ve Teknik Dergisi, 340, pp. 50-55.
  5. Kamil B. Varınca, Gamze Varank, "Rüzgar Kaynaklı Enerji Üretim Sistemlerinde Çevresel Etkilerin Değerlendirilmesi ve Çözüm Önerileri", Yeni ve Yenilenebilir Enerji Kaynakları / Enerji Yönetimi Sempozyumu, pp. 367-376, 2005
  6. Sudirman, R., Ashenayi, K. ve Golbaba, M. (2012). Comparison of methods used for forecasting solar radiation. IEEE Green Technologies Conference,1-3.
  7. Martin, L., Zarzalejo, L.F., Polo, J., Navarro, A., Marchante, R. ve Cony, M. (2010). Prediction of global solar irradiance based on time series analysis: Application to solar thermal power plants energy production planning. Solar Energy, 84 (10), pp. 1772-1781.
  8. Kamadinata, J. O., Ken, T. L., & Suwa, T. (2017, April). Global Solar Radiation Prediction Methodology using Artificial Neural Networks for Photovoltaic Power Generation Systems. In SMARTGREENS, pp. 15-22).
  9. Özdemir, S. (2018). Random Forest Yöntemi kullanılarak potansiyel dağılım modellemesi ve haritalaması: Yukarıgökdere Yöresi örneği. Turkish Journal of Forestry, 19(1), 51-56.
  10. Dragićević, S., & Vučković, N.M. (2007). Evaluation of distributional solar radiation parameters of Čačak using long-term measured global solar radiation data. Thermal Science, 11, pp. 125-134.
  11. Premalatha, N., & Valan Arasu, A. (2016). Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms. Journal of applied research and technology, 14 (3), pp. 206-214.
  12. Yadav, A. K., & Chandel, S. S. (2014). Solar radiation prediction using Artificial Neural Network techniques: A review. Renewable and sustainable energy reviews, 33, pp. 772-781.
  13. Kalogirou, S. A. (2013). Solar energy engineering: processes and systems. Academic Press.
  14. Khalil, S.A. ve Shaffie, A.M. (2013). A comparative study of total, direct and diffuse solar irradiance by using different models on horizontal and inclined surfaces for Cairo, Egypt. Renewable and Sustainable Energy Reviews, 27, 853-863.
  15. Chen, C., Duan, S., Cai, T., Liu, B., & Hu, G. (2011). Smart energy management system for optimal microgrid economic operation. IET renewable power generation, 5 (3), pp. 258-267.
  16. Gumus, B., & Kilic, H. (2018). Time dependent prediction of monthly global solar radiation and sunshine duration using exponentially weighted moving average in southeastern of Turkey. Thermal Science, 22 (2), pp.943-951.
  17. Beyazıt, N. İ., Ünal, F., & Bulut, H. (2019). Modeling of the hourly horizontal solar diffuse radiation in Şanliurfa, Turkey. Thermal Science, (00), 274-274.
  18. Maghrabi, A. H. (2009). Parameterization of a simple model to estimate monthly global solar radiation based on meteorological variables, and evaluation of existing solar radiation models for Tabouk, Saudi Arabia. Energy conversion and management, 50 (11), pp.2754-2760.
  19. Che, H. Z., Shi, G. Y., Zhang, X. Y., Arimoto, R., Zhao, J. Q., Xu, L., ... & Chen, Z. H. (2005). Analysis of 40 years of solar radiation data from China, 1961-2000. Geophysical Research Letters, 32 (6), pp. 1-5.
  20. Katiyar, A. K., & Pandey, C. K. (2010). Simple correlation for estimating the global solar radiation on horizontal surfaces in India. Energy, 35 (12), pp. 5043-5048.
  21. Draper, N.R.,H. Smith, Applied regression analysis. John Wiley & Sons, 1998.
  22. Ren, S., Cao, X., Wei, Y., & Sun, J., Global refinement of random forest. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 723-730, 2015.
  23. Biau, G., & Scornet, E.,. A random forest guided tour. Test, Vol. 25, Issue 2 , Pages 197-227, 2016.
  24. Criminisi, A., Shotton, J., & Konukoglu, E. (2012). Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Foundations and Trends® in Computer Graphics and Vision, 7 (2-3), pp. 81-227, 2012.
  25. Ahmad, M. W., Mourshed, M., & Rezgui, Y. . Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption. Energy and Buildings, Vol. 147, Pages 77-89, 2017.
  26. Criminisi, A., Shotton, J., & Konukoglu, E. (2012). Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Foundations and Trends® in Computer Graphics and Vision, Vol. 7, Issue 2-3, Pages 81-227, 2012.
  27. Solar Energy in Honolulu, www.solarenergylocal.com/states/hawaii/honolulu/
  28. Ulgen, K., & Hepbasli, A. (2004). Solar radiation models. Part 2: Comparison and developing new models. Energy Sources, 26 (5), pp. 521-530.
  29. Wu, G., Liu, Y., & Wang, T. (2007). Methods and strategy for modeling daily global solar radiation with measured meteorological data-A case study in Nanchang station, China. Energy conversion and management, 48(9), pp.2447-2452.
  30. Yao, W., Zhang, C., Hao, H., Wang, X., & Li, X. (2018). A support vector machine approach to estimate global solar radiation with the influence of fog and haze. Renewable Energy, 128, pp.155-162.
  31. Zhang, Q., Cui, N., Feng, Y., Jia, Y., Li, Z., & Gong, D. (2018). Comparative analysis of global solar radiation models in different regions of China. Advances in meteorology, 2018, pp.1-22.
  32. Almorox, J. (2011). Estimating global solar radiation from common meteorological data in Aranjuez, Spain. Turkish journal of physics, 35(1), pp.53-64.