Güneş Işınım Tahmini için Farklı Güneşlenme Durumlarından Faydalanan Hibrit Bir Yöntem Tasarımı

Year 2022, Volume: 22 Issue: 3, 588 - 596, 30.06.2022

Fatma Ehmeind Maham , Emre Akarslan

https://doi.org/10.35414/akufemubid.1074290

Cited By: 1

Abstract

Güneş enerjisinin sürekli genişlemesi, radyasyonun doğru tahminini önemli bir konu haline getirmiştir. Güneş enerjisi üretiminin doğru bir tahmini, fotovoltaik (PV) ve rüzgar jeneratörlerinin akıllı şebekelere etkin entegrasyonu için çok önemlidir. Güneş enerjisinin kesintili doğası, yenilenebilir enerji sistemi operatörleri için operasyonel planlama ve zamanlama açısından birçok zorluk teşkil etmektedir. Bu nedenle güneş ışınımının hibrit yöntemlerle tahmin edilmesi yaygınlaşmaktadır. Bu yazıda, güneş radyasyonunu tahmin etmek için bir hibrit yöntem önerilmiş olup, burada tahmin modeli açıklık indeksine dayalı olarak belirlenir. Çalışmada, Mardin ilinin Türkiye Meteoroloji Genel Müdürlüğünden (TMGM) elde edilen iki yıllık güneş radyasyonu verileri kullanılmıştır. Tahmin edici olarak YSA, NARX ağları ve Ridge regresyon yöntemleri kullanılmış ve çalışmanın ilk aşamasında eğitim verileri her üç yaklaşımla da modellenmiştir. Bulutluluk indeksi için, az bulutlu, bulutlu ve çok bulutluya karşılık gelecek şekilde üç aralık belirlenmiştir. Tahmin edici olarak kullanılan üç yöntem ile eğitim verisi modellenmiş ve her bir yöntemin belirlenen her bir bulutluluk indeksi aralığındaki başarısı incelenmiştir. Sonuç olarak, hibrit tahmin algoritmasında, önce yapay sinir ağları kullanılarak açıklık indeksi tahmin edilir ve daha sonra tahmin edilen açıklık indeksi aralığında en başarılı model kullanılarak gelecekteki güneş radyasyonu değeri tahmin edilir. Deneysel sonuçlar, önerilen hibrit yöntem ile modellerin bireysel olarak kullanıldığı duruma göre daha başarılı tahminler yapıldığını göstermektedir.

Keywords

Güneş Işınımı, Hibrit Yöntem, Yapay Sinir Ağları, Ridge Regresyon, Bulutluluk İndeksi, NARX

Design of a Hybrid Method Exploiting Different Insolation States for Solar Radiation Forecasting

Abstract

The constant expansion of solar energy has made the accurate forecasting of radiation an important issue. An accurate prediction of solar energy production is crucial for the effective integration of photovoltaic (PV) and wind generators in smart grids. The intermittent nature of solar energy poses many challenges to renewable energy system operators in terms of operational planning and scheduling. For this reason, forecasting solar radiation by means of the hybrid methods is becoming widespread. In this paper, a hybrid method for predicting solar radiation is proposed, wherein the prediction model is determined based on the clearness index. The study used two-year solar radiation data of the province of Mardin obtained from the Turkish State Meteorological Service (TSMS). As predictors, ANN, NARX networks, and Ridge regression methods were used, and the training data were modeled with all three approaches in the first stage of the study. The clearness index was determined into three ranges; slightly cloudy, cloudy, and mostly cloudy. The training data were modeled with three methods used as estimators, and the success of each method was examined in each defined clearness index range. As a result, in the hybrid prediction algorithm, the clearness index is first estimated using artificial neural networks, and then the future solar radiation value is predicted by using the most successful model within the predicted clearness index range. Experimental results show that more successful predictions are made with the proposed hybrid method than when models are used individually.

Keywords

Solar Radiation, Hybrid Method, Artificial Neural Networks, Ridge Regression, Clearness index, NARX

References

• Akarslan, E. and Hocaoğlu, F.O., 2018. Bir Fotovoltaik Güç Sisteminin Üretiminin Çok Boyutlu Tahmin Filtreleri ile Modellenmesi. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 18(2), 516-522.
• Akarslan, E. and Hocaoglu, F.O., 2017. A novel method based on similarity for hourly solar irradiance forecasting. Renewable Energy, 112, 337-346.
• Assas, O., Bouzgou, H., Fetah, S., Salmi, M. and Boursas, A., 2014, January. Use of the artificial neural network and meteorological data for predicting daily global solar radiation in Djelfa, Algeria. In 2014 International Conference on Composite Materials & Renewable Energy Applications (ICCMREA) (1-5). IEEE.
• Al-Enezi, J.R., Abbod, M.F. and Alsharhan, S., 2010. Artificial immune systems-models, algorithms and applications. International Journal of Research and Reviews in Applied Sciences (IJRRAS), 3(2), 118-131.
• Azimi, R., Ghayekhloo, M. and Ghofrani, M., 2016. A hybrid method based on a new clustering technique and multilayer perceptron neural networks for hourly solar radiation forecasting. Energy Conversion and Management, 118, 331-344.
• Boussaada, Z., Remaci, A., Curea, O., Camblong, H. and Bellaaj, N., 2017, June. Prediction of the daily direct solar radiation using nonlinear autoregressive exogenous (narx) network model. In SEEP 2017-10th International Conference on Sustainable Energy and Environmental Protection.
• Bounoua, Z. and Mechaqrane, A., 2018, December. Prediction of daily global horizontal solar irradiation using artificial neural networks and commonly measured meteorological parameters. In AIP Conference Proceedings, 2056(1), 020024. AIP Publishing LLC.
• Dong, Z., Yang, D., Reindl, T. and Walsh, W.M., 2014. Satellite image analysis and a hybrid ESSS/ANN model to forecast solar irradiance in the tropics. Energy Conversion and Management, 79, 66-73.
• Di Piazza, A., Di Piazza, M.C. and Vitale, G., 2016. Solar and wind forecasting by NARX neural networks. Renewable Energy and Environmental Sustainability, 1, 39.
• Hepbasli, A. and Alsuhaibani, Z., 2011. A key review on present status and future directions of solar energy studies and applications in Saudi Arabia. Renewable and sustainable energy reviews, 15(9), 5021-5050.
• Hoerl, A.E. and Kennard, R.W., 1970. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55-67.
• Haykin, S. and Network, N., 2004. A comprehensive foundation. Neural networks, 2(2004), 41.
• Jain, A., Mehta, R. and Mittal, S.K., 2011. Modeling impact of solar radiation on site selection for solar PV power plants in India. International Journal of Green Energy, 8(4), 486-498.
• Khorasanizadeh, H., Mohammadi, K. and Aghaei, A., 2014. The potential and characteristics of solar energy in Yazd Province, Iran. Iranica Journal of Energy & Environment, 5(2), 173-183.
• Kumar, R. and Umanand, L., 2005. Estimation of global radiation using clearness index model for sizing photovoltaic system. Renewable Energy, 30(15), 2221-2233.
• Liu, B.Y. and Jordan, R.C., 1960. The interrelationship and characteristic distribution of direct, diffuse and total solar radiation. Solar energy, 4(3), 1-19.
• Mohanty, S., Patra, P.K. and Sahoo, S.S., 2015, December. Prediction of global solar radiation using nonlinear auto regressive network with exogenous inputs (narx). In 2015 39th National Systems Conference (NSC) (1-6). IEEE.
• Nazaripouya, H., Wang, B., Wang, Y., Chu, P., Pota, H.R. and Gadh, R., 2016, May. Univariate time series prediction of solar power using a hybrid wavelet-ARMA-NARX prediction method. In 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D) (1-5). IEEE.
• Owusu, P.A. and Asumadu-Sarkodie, S., 2016. A review of renewable energy sources, sustainability issues and climate change mitigation. Cogent Engineering, 3(1), 1167990.
• Pisoni, E., Farina, M., Carnevale, C. and Piroddi, L., 2009. Forecasting peak air pollution levels using NARX models. Engineering Applications of Artificial Intelligence, 22(4-5), 593-602.
• Sansa, I. and Bellaaj, N.M., 2018. Solar Radiation Prediction Using NARX Model. In Advanced Applications for Artificial Neural Networks. IntechOpen.
• Tikyaa, E.V., Echi, M.I., Isikwue, B.C. and Amah, A.N., 2018. A hybrid SARIMA-NARX nonlinear dynamics model for predicting solar radiation in Makurdi. International Journal of Mathematics and Computer Science, 4(2), 35-47.
• Voyant, C., Muselli, M., Paoli, C. and Nivet, M.L., 2011. Optimization of an artificial neural network dedicated to the multivariate forecasting of daily global radiation. Energy, 36(1), 348-359.
• Yadav, A.K. and Chandel, S.S., 2014. Solar radiation prediction using Artificial Neural Network techniques: A review. Renewable and sustainable energy reviews, 33, 772-781.
• Zameer, A., Shahid, F., Afzal, M., & Hassan, M., 2020. Intelligent forecast models for daily solar energy prediction. https://www.researchgate.net/profile/Aneela-Zameer/publication/346555161. Accessed: 10.01.2022
• Zhang, W.Y., Zhao, Z.B., Han, T.T. and Kong, L.B., 2011, September. Short term wind speed forecasting for wind farms using an improved autoregression method. In 2011 International Conference of Information Technology, Computer Engineering and Management Sciences , 1, 195-198.