THERMAL SCIENCE

International Scientific Journal

Authors of this Paper

External Links

Hakan Kör

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
random forest, solar radiation, prediction model
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
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2021, VOLUME 25, ISSUE Special issue 1, PAGES [31 - 39]
REFERENCES
  1. Kallioglu, M. A., Niğde İli İçin Yatay Düzenleme Gelen Günlük Tüm. Yayılı ve Direkt Güneş Işınımını Hesaplama Modeli Geliştirilmesi (Improving a Model for Calculating Daily Global, Diffuse and Direct Solar Radiation on Horizontal Surfaces for Niğde – in Turkish), Ph. D. thesis, Nigde University, Nigde, Turkey, 2014
  2. Gurbuz, A., Enerji Piyasası İçerisinde Yenilenebilir (Temiz) Enerji Kaynaklarının Yeri ve Önemi”, Proceedings, (The Place and Importance of Renewable (Clean) Energy Resources in the Energy Market – in Turkish), 5th Inter. Advanced Technologies Symp., Karabuk, Turkey, 2009
  3. Rifkin, J., Howard, T., Entropi Dünyaya Yeni Bir Bakış (A New Look at the Entropic World – in Turkish), Yayınevi, Istanbul, Turkey, 1997
  4. Ultanır, M. O., Solar Energy is on the Verge of the Century, Bilim ve Teknik Dergisi, 340 (1996), pp. 50-55
  5. Varinca, K. B., Varank, G., Rüzgar Kaynaklı Enerji Üretim Sistemlerinde Çevresel Etkilerin Değerlendirilmesi ve Çözüm Önerileri (Assessment of Environmental Impacts and Solution Suggestions in Wind Based Power Generation Systems – in Turkish), Proceedings, New and Renewable Energy Resources/Energy Management Symposium, Kayseri, Turkey, pp. 367-376, 2005
  6. Sudirman, R., et al., Comparison of Methods Used for Forecasting Solar Radiation, Proceedings, IEEE Green Technologies Conference, Tuksa, Okla., USA, 2012, pp. 1-3
  7. Martin, L., et al., Prediction of Global Solar Irradiance Based on Time Series Analysis: Application Solar Thermal Power Plants Energy Production Planning, Solar Energy, 84 (2010), 10, pp. 1772-1781
  8. Kamadinata, J. O., et al., Global Solar Radiation Prediction Methodology using Artificial Neural Networks for Photovoltaic Power Generation Systems, Proceedings, SMARTGREENS, Porto, Portugal, 2017, pp. 15-22
  9. Ozdemir, S., Random Forest Yöntemi kullanılarak potansiyel dağılım modellemesi ve haritalaması: Yukarıgökdere Yöresi örneği (Potential Distribution Modeling and Mapping Using Random Forest Method: the Case of Yukarigökdere Region – in Turkish), Turkish Journal of Forestry, 19 (2018), 1, pp. 51-56
  10. Dragićević, S., Vučković, N. M., Evaluation of Distributional Solar Radiation Parameters of Čačak Using Long-Term Measured Global Solar Radiation Data, Thermal Science, 11 (2007), 4, pp. 125-134
  11. Premalatha, N., Valan, A. A., Prediction of Solar Radiation for Solar Systems by Using ANN Models with Different Back Propagation Algorithms, Journal of Applied Research and Technology, 14 (2016), 3, pp. 206-214
  12. Yadav, A. K., Chandel, S. S., Solar Radiation Prediction Using Artificial Neural Network Techniques: A Review, Renewable and Sustainable Energy Reviews, 33 (2014), May, pp. 772-781
  13. Kalogirou, S. A., Solar Energy Engineering: Processes and Systems, Academic Press, New York, USA
  14. Khalil, S. A., Shaffie, A. M., 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 (2013), Nov., pp. 853-863
  15. Chen, C., et al., Smart Energy Management System for Optimal Microgrid Economic Operation, IET Renewable Power Generation, 5 (2011), 3, pp. 258-267
  16. Gumus, B., Kilic, H., Time Dependent Prediction of Monthly Global Solar Radiation and Sunshine Duration Using Exponentially Weighted Moving Average in Southeastern of Turkey, Thermal Science, 22 (2018), 2, pp. 943-951
  17. Beyazıt, N. I., et al., Modelling of the Hourly Horizontal Solar Diffuse Radiation in Sanliurfa, Turkey, Thermal Science, 24 (2019), 2, pp. 939-950
  18. Maghrabi, A. H., 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 (2009), 11, pp. 2754-2760
  19. Che, H. Z., et al., Analysis of 40 Years of Solar Radiation Data from China, 1961-2000, Geophysical Research Letters, 32 (2005), 6, pp. 1-5
  20. Katiyar, A. K., Pandey, C. K., Simple Correlation for Estimating the Global Solar Radiation on Horizontal Surfaces in India, Energy, 35 (2010), 12, pp. 5043-5048
  21. Draper, N. R., Smith, H., Applied Regression Analysis, John Wiley & Sons, New York, USA, 1998
  22. Ren, S., et al., Global Refinement of Random Forest, Proceedings, IEEE Conference on Computer Vision and Pattern Recognition, Boston, Mass., USA, 2015, pp. 723-730
  23. Biau, G., Scornet, E., A Random Forest Guided Tour, Test, 25 (2016), 2, pp. 197-227
  24. Criminisi, A., et al., 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 (2012), 2-3, pp. 81-227
  25. Ahmad, M. W., et al., Trees vs Neurons: Comparison between Random Forest and ANN for High-Resolution Prediction of Building Energy Consumption, Energy and Buildings, 147 (2017), July, pp. 77-89
  26. ***, Solar Energy in Honolulu, https://www.solarenergylocal.com/states/hawaii/honolulu/
  27. Ulgen, K., Hepbasli, A., Solar Radiation Models – Part 2: Comparison and Developing New Models, Energy Sources, 26 (2004), 5, pp. 521-530
  28. Wu, G., et al., Methods and Strategy for Modelling Daily Global Solar Radiation with Measured Meteorological Data – A Case Study in Nanchang Station, China, Energy Conversion and Management, 48 (2007), 9, pp. 2447-2452
  29. Yao, W., et al., A Support Vector Machine Approach to Estimate Global Solar Radiation with the Influence of Fog and Haze, Renewable Energy, 128 (2018), Part A, pp. 155-162
  30. Zhang, Q., et al., Comparative Analysis of Global Solar Radiation Models in Different Regions of China, Advances in Meteorology, 2018 (2018), ID 3894831
  31. Almorox, J., Estimating Global Solar Radiation from Common Meteorological Data in Aranjuez, Spain, Turkish Journal of Physics, 35 (2011), 1, pp. 53-64
PDF VERSION [DOWNLOAD]
Global solar radiation prediction model with random forest algorithm

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence