DEVELOPMENT NEW MODEL for FORECASTING SOLAR RADIATION by USING RATIONAL APPROACH

Year 2022, Issue: 051, 230 - 239, 31.12.2022

Ayşe Gül Kaplan

Abstract

In this study, solar energy potential of south region of Turkey was assessed statistically by using the Solar Energy Potential Map of Turkey data for one year. A new model was developed by using rational approach for global solar radiation (SR) estimation. Calculations was performed in Matlab program. To demonstrate the efficiency of new model the four different statistical indicators were used. The obtained results reveal that the new proposed model give very appropriate results for SR estimation for mentioned region.

Keywords

Solar energy, Solar radiation, Rational approach, Statistical indicators.

Thanks

This research received no specific grants from any funding agency in public, commercial or non-profit sectors.

References

• [1] Yiğit, E., Özkaya, U., Öztürk, Ş., Singh, D., and Gritli, H. (2021). Automatic detection of power quality disturbance using convolutional neural network structure with gated recurrent unit. Mobile Information Systems, 2021, Article ID 7917500, 11 pages.
• [2] Kaplan, A. G., and Kaplan, Y. A., (2020). Developing of the new models in solar radiation estimation with curve fitting based on moving least-squares approximation. Renewable Energy, 146, 2462-2471.
• [3] Kaplan, Y. A., (2018). A new model for predicting the global solar radiation. Environmental Progress & Sustainable Energy, 37, 870-880.
• [4] Zhou, Y., Liu, Y., Wang, D., Liu, X., and Wang, Y. (2021). A review on global solar radiation prediction with machine learning models in a comprehensive perspective. Energy Conversion and Management, 235, 113960.
• [5] Mustafa, J., Alqaed, S., Almehmadi, F. A., and Jamil, B. (2022). Development and comparison of parametric models to predict global solar radiation: a case study for the southern region of Saudi Arabia. Journal of Thermal Analysis and Calorimetry, 147, 9559–9589.
• [6] Solar Energy Maps of Turkey, https://gepa.enerji.gov.tr/MyCalculator/, (acces date: December 2022)
• [7] Zang, H., Cheng, L., Ding, T., Cheung, K. W., Wang, M., Wei, Z., and Sun, G. (2019). Estimation and validation of daily global solar radiation by day of the year-based models for different climates in China. Renewable Energy, 135, 984-1003.
• [8] Husain, S., and Khan, U. A. (2022). Development of machine learning models based on air temperature for estimation of global solar radiation in India. Environmental Progress & Sustainable Energy, 41(4), e13782.
• [9] Feng, Y., Gong, D., Zhang, Q., Jiang, S., Zhao, L., and Cui, N. (2019). Evaluation of temperature-based machine learning and empirical models for predicting daily global solar radiation. Energy Conversion and Management, 198, 111780.
• [10] Almorox, J., Voyant, C., Bailek, N., Kuriqi, A., and Arnaldo, J. A. (2021). Total solar irradiance's effect on the performance of empirical models for estimating global solar radiation: An empirical-based review. Energy, 236, 121486.
• [11] MathWorks Help Center, https://ch.mathworks.com/help/curvefit/rational.html.
• [12] Bamisile, O., Oluwasanmi, A., Ejiyi, C., Yimen, N., Obiora, S., and Huang, Q. (2022). Comparison of machine learning and deep learning algorithms for hourly global/diffuse solar radiation predictions. International Journal of Energy Research, 46, 10052-10073.
• [13] Karaman, O. A., Agır, T. T., and Arsel, I. (2021). Estimation of solar radiation using modern methods. Alexandria Engineering Journal, 60, 2447-2455.
• [14] Makade, R. G., Chakrabarti, S., and Jamil, B. (2021). Development of global solar radiation models: A comprehensive review and statistical analysis for Indian regions. Journal of Cleaner Production, 293, 126208.
• [15] Oztürk, M., Ozek, N., and Berkama, B. (2012). Comparison of Some Existing Models for Estimating Monthly Average Daily Global Solar Radiation for Isparta. Pamukkale University Journal of Engineering Sciences, 18, 13-27.
• [16] Khorasanizadeh, H., Mohammadi, K., and Mostafaeipour, A. (2014). Establishing a diffuse solar radiation model for determining the optimum tilt angle of solar surfaces in Tabass, Iran. Energy Conversion and Management, 78, 805-814.
• [17] Akossou, A. Y. J., and Palm, R. (2013). Impact of data structure on the estimators R-square and adjusted R-square in linear regression. International Journal of Mathematics and Computation, 20, 84-93.