HOURLY GLOBAL SOLAR RADIATION ESTIMATION BASED ON MACHINE LEARNING METHODS IN ESKISEHIR

Year 2020, Volume: 21 Issue: 2, 294 - 313, 15.06.2020

Massa Alsafadı Ümmühan Başaran Filik

https://doi.org/10.18038/estubtda.650497

Cited By: 4

Abstract

Due to the increasing importance of knowing the global
solar radiation (GSR) amount incident on solar panels; short term data, such as
hourly global solar radiation (HGSR), is essentially required to obtain more accurate
and reliable power generation prediction. Nowadays, Machine Learning (ML)
methods are becoming a huge trend for data forecasting. Therefore, in this
paper, a comparison between Collares-Pereira & Rabl empirical model modified
by Gueymard (CPRG) and ML methods for HGSR estimation in Eskişehir city
in Turkey is conducted. Artificial Neural Network (ANN), Regression Tree (RT)
and Support Vector Regression (SVR) are ML methods that are used to predict
HGSR. Besides, hourly metrological and geographical parameters for the year
2014 are taken as inputs in the training models. The inputs are solar time,
solar hour angle, Julian day number, daily GSR, altitude, longitude, latitude, hourly
average humidity, hourly temperature and hourly pressure. To demonstrate these
techniques, a comparison is implemented using MATLAB software with the help of
existing toolboxes. Finally, this study proved that ML methods outperform the
CPRG model and not to mention that they have far more accurate results.
Although almost all ML models gave similar results, SVR
was the best among them with a correlation coefficient of 0.979532 for the
training set and 0.978244 for the testing set. In a nutshell, ML are successful
methods that should be to be taken into consideration to perfectly estimate
HGSR now and in the future in the field of solar renewable energy estimation.

Keywords

Hourly global solar radiation, Machine learning, Artificial neural network, Regression tree, Support vector regression.

References

• [1] Başaran Filik Ü, Filik T and Gerek Ö, New Electric Transmission Systems: Experiences, in Handbook of Clean Energy Systems, Wiley, 2015.
• [2] Shaddel M, Javan DS and Baghernia P. Estimation of hourly global solar irradiation on tilted absorbers from horizontal one using Artificial Neural Network for case study of Mashhad, vol. 53, Elsevier Ltd, 2016, pp. 59-67.
• [3] Khosravi A, Koury RN, Machado L and Pabon JJ. Prediction of hourly solar radiation in Abu Musa Island using machine learning algorithms, Journal of Cleaner Production, vol. 176, pp. 63-75, 2018.
• [4] Voyant C, Notton G, Kalogirou S, et al. Machine learning methods for solar radiation forecasting: A review, vol. 105, Elsevier Ltd, 2017, pp. 569-582.
• [5] Kaba K, Sarıgül M, Avcı M, et al. Estimation of daily global solar radiation using deep learning model, Energy, vol. 162, pp. 126-135, 2018.
• [6] Sözen A, Arcaklioğlu E and Özalp M. Estimation of solar potential in Turkey by artificial neural networks using meteorological and geographical data, Energy Conversion and Management, vol. 45, no. 18-19, pp. 3033-3052, 2004.
• [7] Elminir HK, Areed FF and Elsayed TS. Estimation of solar radiation components incident on Helwan site using neural networks, Solar Energy, 2005; vol. 79, no. 3, pp. 270-279.
• [8] Angela K, Taddeo S and James M. Predicting Global Solar Radiation Using an Artificial Neural Network Single-Parameter Model, Advances in Artificial Neural Systems, vol. 2011, pp. 1-7.
• [9] Salem H, Pharma H, Abdelhafez E, et al. Prediction of Hourly Solar Radiation in Amman Jordan by Using Artificial Neural Networks, Int. J. of Thermal & Environmental Engineering, 2017; vol. 14, no. 2, pp. 103-108.
• [10] Troncoso A, Salcedo-Sanz S, Casanova-Mateo C, et al. Local models-based regression trees for very short-term wind speed prediction, Renewable Energy, 2015; vol. 81, pp. 589-598.
• [11] Mori H and Takahashi A. A data mining method for selecting input variables for forecasting model of global solar radiation, in Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference, 2012.
• [12] Chen JL, Li GS and Wu SJ. Assessing the potential of support vector machine for estimating daily solar radiation using sunshine duration, Energy Conversion and Management, 2013; vol. 75, pp. 311-318.
• [13] Mohammadi K, Shamshirband S, Anisi MH, et al. Support vector regression based prediction of global solar radiation on a horizontal surface, Energy Conversion and Management, 2015; vol. 91, pp. 433-441.
• [14] Ramedani Z, Omid M, Keyhani A, et al. A comparative study between fuzzy linear regression and support vector regression for global solar radiation prediction in Iran, Solar Energy, 2014; vol. 109, no. 1, pp. 135-143,
• [15] Jiang H and Dong Y. Global horizontal radiation forecast using forward regression on a quadratic kernel support vector machine: Case study of the Tibet Autonomous Region in China, Energy, 2017; vol. 133, pp. 270-283.
• [16] Bayrakçı HC, Demircan C and Keçebaş A. The development of empirical models for estimating global solar radiation on horizontal surface: A case study, vol. 81, Elsevier Ltd, 2018, pp. 2771-2782.
• [17] Whillier A. The Determination of Hourly Values of Total Solar Radiation from Daily Summations, Arch für Meteorol, Geophys und Bioklimatol, 1956; vol. 7, p. 197–204.
• [18] Yao W, Li Z, Xiu T, Lu Y and Li X. New decomposition models to estimate hourly global solar radiation from the daily value, Solar Energy, 2015; vol. 120, pp. 87-99.
• [19] Liu BYH and Jordan RC. The Interrelationship and of Direct, Diffuse and Characteristic Distribution Total Solar Radiation, Solar Energy, 1960; vol. 4, no. 3, p. 1–19.
• [20] Collares-Pereira M and Rabl A. The average distribution of solar radiation-correlations between diffuse and hemispherical and between daily and hourly insolation values, Solar Energy, 1979; vol. 22, no. 2, pp. 155-164.
• [21] Gueymard C. Mean daily averages of beam radiation received by tilted surfaces as affected by the atmosphere, Solar Energy, 1986; vol. 37, no. 4, pp. 261-267.
• [22] Garg H and Garg S. Improved correlation of daily and hourly diffuse radiation with global radiation for Indian stations, Solar & Wind Technology, 1987; vol. 4, no. 2, pp. 113-126.
• [23] Jain P. Comparison of techniques for the estimation of daily global irradiation and a new technique for the estimation of hourly global irradiation, Solar & Wind Technology, 1984; vol. 1, no. 2, pp. 123-134.
• [24] Jain P. Estimation of monthly average hourly global and diffuse irradiation, Solar & Wind Technology, 1988; vol. 5, no. 1, pp. 7-14.
• [25] El shazly SM. Estimation of Hourly and Daily Global Solar Radiation at Clear Days Using an Approach Based on Modified Version of Gaussian Distribution, Advances in Atmospheric Sciences, 1996; vol. 13, no. 3, p. 349–358.
• [26] Newell T. Simple models for hourly to daily radiation ratio correlations, Solar Energy, 1983; vol. 31, no. 3, pp. 339-342.
• [27] Ayvazoğluyüksel Ö and Başaran Filik Ü. Estimation methods of global solar radiation, cell temperature and solar power forecasting: A review and case study in Eskişehir, Renewable and Sustainable Energy Reviews, 2018; vol. 91, pp. 639-653.
• [28] Maleki SAM, Hizam H and Gomes C. Estimation of hourly, daily and monthly global solar radiation on inclined surfaces: Models re-visited, Energies, 2017; vol. 10, no. 1.
• [29] Kumar R, Aggarwal RK and Sharma JD. Comparison of regression and artificial neural network models for estimation of global solar radiations, vol. 52, Elsevier Ltd, 2015, pp. 1294-1299.
• [30] Khatib T, Mohamed A, Sopian K, et al. Assessment of artificial neural networks for hourly solar radiation prediction, International Journal of Photoenergy, vol. 2012, 2012.
• [31] Alzahrani A, Shamsi P, Dagli C and Ferdowsi M, Solar Irradiance Forecasting using Deep Neural Networks, in Procedia Computer Science, 2017.
• [32] Marsland S, Machine Learning & Pattern Recognition: An Algorithmic Perspective, Chapman & Hall/CRC, 2014.
• [33] Breiman L, Friedman JH, Olshen RA, et al. Classification and regression trees., Wadsworth, Inc., 1984.
• [34] Mori H and Kosemura N. Optimal Regression Tree Based Rule Discovery for Short-term Load Forecasting, 2000.
• [35] Lauret P, Voyant C, Soubdhan T, et al. A benchmarking of machine learning techniques for solar radiation forecasting in an insular context, Solar Energy, 2015; vol. 112, pp. 446-457, 1 2.
• [36] Dong Z, Yang D, Reindl T, et al. A novel hybrid approach based on self-organizing maps, support vector regression and particle swarm optimization to forecast solar irradiance, Energy 2015; vol. 82, pp. 570-577.
• [37] Vapnik V. The Nature of Statistical Learning Theory, Springer-Verlag New York, 1995.
• [38] Smola AJ and Schölkopf B. A tutorial on support vector regression, Kluwer Academic Publishers, 2004.
• [39] Deep Learning Toolbox, MathWorks, 2019. [Online]. Available: https://www.mathworks.com/products/deep-learning.html.
• [40] Machine learning and Statistics toolbox, MathWorks, 2019. [Online]. Available: https://www.mathworks.com/products/statistics.html.