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Bilal GÜMÜŞ1 Hibetullah KILIÇ


This paper proposes a new approach for prediction of Global Solar Radiation and Sunshine Duration based on earlier years of data for the eastern region of Turkey which has a high potential of solar energy. The proposed method predicts the basic parameters using time series and an analysis method. This method is Exponentially Weighted Moving Average (EWMA). This model estimates next years’ Global Solar Radiation and Sunshine Duration and is evaluated by statistical parameters s, “Mean Absolute Percentage Error” (MAPE) and “Coefficient of Determination,” (R2) to examine the success of the proposed technique. In our study, the result shows that this method is effective in predicting Global Solar Radiation and Sunshine Duration as regards of MAPE and R2. The calculated MAPEs which are between 0 – 10 kWh/m2 per day were assumed excellent and R2s were found significant per every year.

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GÜMÜŞ1, Bilal; KILIÇ, Hibetullah. TIME DEPENDENT PREDICTION OF MONTHLY GLOBAL SOLAR RADIATION AND SUNSHINE DURATION USING EXPONENTIALLY WEIGHTED MOVING AVERAGE IN SOUTHEASTERN OF TURKEY. Thermal Science, [S.l.], mar. 2017. ISSN 2334-7163. Available at: <http://thermal-science.tech/journal/index.php/thsci/article/view/2259>. Date accessed: 20 feb. 2018. doi: https://doi.org/10.2298/TSCI160107228K.
Received 2017-03-07
Accepted 2017-03-13
Published 2017-03-13


[1] M. Pierro, F. Bucci, C. Cornaro, E. Maggioni, a. Perotto, M. Pravettoni, and F. Spada, Model output statistics cascade to improve day ahead solar irradiance forecast, Sol. Energy, vol. 117, (2015), pp. 99–113
[2] I. Petrovic, Z. Simic, and M. Vrazic, Advanced PV Plant Planning based on Measured Energy Production Results - Approach and Measured Data Processing, Adv. Electr. Comput. Eng., vol. 14, (2014), no. 1, pp. 49–54
[3] A. K. Yadav and S. S. Chandel, Solar radiation prediction using Artificial Neural Network techniques: A review, Renew. Sustain. Energy Rev., vol. 33, (2014), pp. 772–781
[4] S. Ravichandra, J.D. Rathnaraj, Analysis of ratio of Global to Extra-Terrestrial Radiation at some Tropical Locations in India, Thermal Science, vol. 20, (2015), pp. 425-433
[5] H. Nazaripouya, B. Wang, Y. Wang, P. Chu, H. R. Pota, and R. Gadh, Univariate Time Series Prediction of Solar Power Using a Hybrid Wavelet-ARMA-NARX Prediction Method, IEEE PES T&D, Dallas, Texas, 2-5 May 2016.
[6] M. A. Shamim, R. Remesan, M. Bray, and D. Han, An improved technique for global solar radiation estimation using numerical weather prediction, J. Atmos. Solar-Terrestrial Phys., vol. 129, (2015), pp. 13–22
[7] A. Adeyemi, Zhongjie Huan and C. Enweremadu, Evaluation of Global Solar Radiation Using Multiple Weather Parameters as Predictors for Soutj Africa Provinces, Thermal Science, vol. 19, (2015), pp. 495-509
[8] K. Bakirci, Models for the estimation of diffuse solar radiation for typical cities in Turkey, Energy, vol. 82, (2015), pp. 827–838
[9] F. Besharat, A. A. Dehghan, and A. R. Faghih, Empirical models for estimating global solar radiation: A review and case study, Renew. Sustain. Energy Rev., vol. 21, (2013), pp. 798–821.
[10] A. Qazi, H. Fayaz, a. Wadi, R. G. Raj, and N. a. Rahim, The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review, J. Clean. Prod., vol. 104, (2015), pp. 1–12
[11] T. C. McCandless, S. E. Haupt, and G. S. Young, A model tree approach to forecasting solar irradiance variability, Sol. Energy, vol. 120, (2015), pp. 514–524
[12] K. N. Shukla, S. Rangnekar, and K. Sudhakar, Comparative study of isotropic and anisotropic sky models to estimate solar radiation incident on tilted surface: A case study for Bhopal, India, Energy Reports, vol. 1, (2015), pp. 96–103
[13] O. Kisi, Modeling solar radiation of Mediterranean region in Turkey by using fuzzy genetic approach, Energy, vol. 64, (2014), pp. 429–436
[14] Y. S. Güçlü, M. Ö. Yeleğen, İ. Dabanlı, and E. Şişman, Solar irradiation estimations and comparisons by ANFIS, Angström–Prescott and dependency models, Sol. Energy, vol. 109, (2014), pp. 118–124
[15] Y. S. Güçlü, İ. Dabanlı, E. Şişman, and Z. Şen, HARmonic–LINear (HarLin) model for solar irradiation estimation, Renew. Energy, vol. 81, (2015), pp. 209–218
[16] Ö. N. Gerek, F. O. Hocaoğlu, and M. Fidan, Harmonic analysis based hourly solar radiation forecasting model, IET Renew. Power Gener., vol. 9, no. 3, (2015), pp. 218–227
[17] J.-K. Park, A. Das, and J.-H. Park, A new approach to estimate the spatial distribution of solar radiation using topographic factor and sunshine duration in South Korea, Energy Convers. Manag., vol. 101, (2015), pp. 30–39
[18] R. C. de Andrade and C. Tiba, Extreme global solar irradiance due to cloud enhancement in northeastern Brazil, Renew. Energy, vol. 86, (2015), pp. 1433–1441
[19] A. Teke and H. B. Yıldırım, Estimating the monthly global solar radiation for Eastern Mediterranean Region, Energy Convers. Manag., vol. 87, (2014), pp. 628–635
[20] C. C. Holt, Forecasting seasonals and trends by exponentially weighted moving averages, Int. J. Forecast., vol. 20, (2014), no. 1, pp. 5–10
[21] F. Kentli, M. Yilmaz, Mathematical Modelling of Two-axis Photovoltaic System with Improved Efficiency, Elektronika Ir Elektrotechnika. vol.21, (2015), no.4, pp. 40-43
[22] Boland John, Time-Series Analysis of Climatic Variables, Solar Energy 55, 1995
[23] P. Brockwell, R. A. Davis, Introduction to Time Series and Forecasting, Springer- Verlag, New York, 1996
[24] B. L. Bowerman, R. T. O’Connell, Time Series Forecasting, Duxbury Press, Boston, 1979
[25] C .Lewis, International and business forecasting methods, Butterworths, London, 1982
[26] J. Smith, Statistical Analysis, Chapman and Hall, the Winchelsea press, Winchelsea, 2012