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Adıyaman ve Gaziantep İstasyonlarındaki Güneşlenme Şiddetinin Destek Vektör Makineleri ile Tahmini

Yıl 2021, Cilt: 8 Sayı: 2, 753 - 769, 31.12.2021
https://doi.org/10.35193/bseufbd.904393

Öz

Güneş enerjisi teknolojilerinin kullanımı, birçok ülkede enerji talebini karşılamak ve sürdürülebilir enerji kaynağı sağlamak amacıyla son yıllarda gelişerek artmaktadır. Bu teknolojilerin verimli kullanılabilmesi için, güneşlenme şiddeti verilerinin doğru bir şekilde belirlenmesi gerekmektedir, böylece yapılacak olan yatırımların verimliliği de önceden belirlenebilecektir. Güneş enerjisi ölçüm cihazlarının yetersizliğinin yanında var olan ekipmanların yenilenme veya onarım maliyetlerinden dolayı, literatürde meteorolojik istasyonlardan elde edilen verilerin girdi parametresi olarak kullanılması ve yapay zekâ yöntemleri ile güneşlenme şiddeti verilerinin hesaplanması yapılmaktadır. Bu çalışmada, Adıyaman ve Gaziantep istasyonlarına ait, sıcaklık, nem, ortalama basınç, rüzgâr, aylık açık gün sayısı ve takvim ayı gibi farklı girdi parametreleri kullanılarak, bu istasyonlara ait aylık ortalama güneşlenme şiddeti tahmin edilmeye çalışılmıştır. Aylık ortalama güneşlenme şiddetinin tahmin edilmesi için, destek vektör makineleri yönteminin üç farklı çekirdek fonksiyonu (Radyal, Lineer ve Polinom) kullanılmıştır. Ele alınan çekirdek fonksiyonlarının güneşlenme şiddetini tahmin etmedeki başarısında, belirlilik katsayısı (R2), Karekök Ortalama Karesel Hata (KOKH), Ortalama Mutlak Yüzde Hata (OMYH), Nash–Sutcliffe verimlilik katsayısı (NSE) ve Yüzde Hata (PBIAS) parametreleri başarı kriteri olarak tercih edilmiştir. Çalışma sonucunda, destek vektör makinelerinin Radyal ve Polinom çekirdek fonksiyonlarının güneşlenme şiddetini belirlemede genel olarak başarılı sonuçlar verdiği görülmüştür. Ayrıca, girdi parametresi olarak ortalama sıcaklık ve ortalama basıncın kullanılmasının tahmin modellerinin performansını arttırdığı belirlenmiştir.

Kaynakça

  • Senkal, O. & Kuleli, T. (2009). Estimation of solar radiation over Turkey using artificial neural network and satellite data. Applied Energy, 86(7-8), 1222-1228.
  • Badescu, V. (2014). Modeling solar radiation at the earth's surface. Berlin: Springer. 1, 517.
  • Droogers, P. & Allen, R. G. (2002). Estimating Reference Evapotranspiration Under Inaccurate Data Conditions. Irrigation and Drainage Systems, 16(1), 33-45.
  • Mellit, A. (2008). Artificial Intelligence technique for modelling and forecasting of solar radiation data: a review. International Journal of Artificial intelligence and soft computing, 1(1), 52-76.
  • Besharat, F., Dehghan, A. A. & Faghih, A. R. (2013). Empirical models for estimating global solar radiation: A review and case study. Renewable & Sustainable Energy Reviews, 21, 798-821.
  • Guermoui, M., Abdelaziz, R., Gairaa, K., Djemoui, L. & Benkaciali, S. (2020). New temperature-based predicting model for global solar radiation using support vector regression. International Journal of Ambient Energy, 1-11.
  • Mohsenzadeh Karimi, S., Kisi, O., Porrajabali, M., Rouhani-Nia, F. & Shiri, J. (2020). Evaluation of the support vector machine, random forest and geo-statistical methodologies for predicting long-term air temperature. ISH Journal of Hydraulic Engineering, 26(4), 376-386.
  • Fan, J., Wang, X., Wu, L., Zhou, H., Zhang, F., Yu, X., Lu, X. & Xiang, Y. (2018). Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China. Energy Conversion And Management, 164, 102-111.
  • Shiri, J., Kisi, O., Landeras, G., Lopez, J. J., Nazemi, A. H. & Stuyt, L. C. P. M. (2012). Daily reference evapotranspiration modeling by using genetic programming approach in the Basque Country (Northern Spain). Journal of Hydrology, 414, 302-316.
  • Landeras, G., Lopez, J. J., Kisi, O. & Shiri, J. (2012). Comparison of Gene Expression Programming with neuro-fuzzy and neural network computing techniques in estimating daily incoming solar radiation in the Basque Country (Northern Spain). Energy Conversion and Management, 62, 1-13.
  • Güçlü, Y. S., Yeleğen, M. Ö., Dabanlı, İ. & Şişman, E. (2014). Solar irradiation estimations and comparisons by ANFIS, Angström–Prescott and dependency models. Solar Energy, 109, 118-124.
  • Güçlü, Y. S., Dabanlı, İ. & Şişman, E. (2014). Short-and long-term solar radiation estimation method, In Progress in Exergy, Energy, and the Environment. 527-532.
  • Citakoglu, H. (2015). Comparison of artificial intelligence techniques via empirical equations for prediction of solar radiation. Computers and Electronics in Agriculture, 118, 28-37.
  • Güçlü, Y. S., Dabanlı, İ., Şişman, E. & Şen, Z. (2015). HARmonic–LINear (HarLin) model for solar irradiation estimation. Renewable Energy, 81, 209-218.
  • Belaid, S. & Mellit, A. (2016). Prediction of daily and mean monthly global solar radiation using support vector machine in an arid climate. Energy Conversion and Management, 118, 105-118.
  • Chiteka, K. & Enweremadu, C. C. (2016). Prediction of global horizontal solar irradiance in Zimbabwe using artificial neural networks. Journal of Cleaner Production, 135, 701-711.
  • Bakhashwain, J. M. (2016). Prediction of global solar radiation using support vector machines. International Journal of Green Energy, 13(14), 1467-1472.
  • Hassan, G. E., Youssef, M. E., Mohamed, Z. E., Ali, M. A. & Hanafy, A. A. (2016). New Temperature-based Models for Predicting Global Solar Radiation. Applied Energy, 179, 437-450.
  • Laidi, M., Hanini, S., Rezrazi, A., Yaiche, M. R., El Hadj, A. A. & Chellali, F. (2017). Supervised artificial neural network-based method for conversion of solar radiation data (case study: Algeria). Theoretical and Applied Climatology, 128(1-2), 439-451.
  • Wang, L. C., Kisi, O., Zounemat-Kermani, M., Zhu, Z. M., Gong, W., Niu, Z. G., Liu, H. F. & Liu, Z. J. (2017). Prediction of solar radiation in China using different adaptive neuro-fuzzy methods and M5 model tree. International Journal of Climatology, 37(3), 1141-1155.
  • Hassan, M. A., Khalil, A., Kaseb, S. & Kassem, M. A. (2017). Exploring the potential of tree-based ensemble methods in solar radiation modeling. Applied Energy, 203, 897-916.
  • Basaran, K., Ozcift, A. & Kilinc, D. (2019). A New Approach for Prediction of Solar Radiation with Using Ensemble Learning Algorithm. Arabian Journal for Science and Engineering, 44(8), 7159-7171.
  • Gülşen, K., Sönmez, M. E. & Karabaş, M. (2019). Gaziantep İlinde Güneş Enerjisi Potansiyelinin Analitik Hiyerarşi Süreci Yöntemi (AHP) İle Belirlenmesi. Coğrafya Dergisi, (39), 61-72.
  • Alizamir, M., Kim, S., Kisi, O. & Zounemat-Kermani, M. (2020). A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions. Energy, 197.
  • Muhammed, O. (2020). Türkiye’nin Güneş Enerjisi Potansiyeli ve PV Uygulamalarının Yerel Ölçekte Değerlendirilmesi: Karabük İli Örneği. lnternational Journal of Geography and Geography Education, (42), 482-503.
  • Bilir, L. & Yildirim, N. (2018). Modeling and performance analysis of a hybrid system for a residential application. Energy, 163, 555-569.
  • GEPA. (2020). https://gepa.enerji.gov.tr/MyCalculator/ (Erişim tarihi: 26.03.2021)
  • Vapnik, V. (2013). The nature of statistical learning theory. Springer science & business media.
  • Dibike, Y. B., Velickov, S., Solomatine, D. & Abbott, M. B. (2001). Model induction with support vector machines: Introduction and applications. Journal of Computing in Civil Engineering, 15(3), 208-216.
  • Bray, M. & Han, D. (2004). Identification of support vector machines for runoff modelling. Journal of Hydroinformatics, 6(4), 265-280.
  • Chen, H., Guo, J., Xiong, W., Guo, S. L. & Xu, C. Y. (2010). Downscaling GCMs using the Smooth Support Vector Machine method to predict daily precipitation in the Hanjiang Basin. Advances in Atmospheric Sciences, 27(2), 274-284.
  • Nieto, P. J. G., Torres, J. M., Fernandez, M. A. & Galan, C. O. (2012). Support vector machines and neural networks used to evaluate paper manufactured using Eucalyptus globulus. Applied Mathematical Modelling, 36(12), 6137-6145.
  • Hosseini, S. M. & Mahjouri, N. (2016). Integrating Support Vector Regression and a geomorphologic Artificial Neural Network for daily rainfall-runoff modeling. Applied Soft Computing, 38, 329-345.
  • Khan, M. S. & Coulibaly, P. (2006). Application of support vector machine in lake water level prediction. Journal of Hydrologic Engineering, 11(3), 199-205.
  • Asefa, T., Kemblowski, M., Lall, U. & Urroz, G. (2005). Support vector machines for nonlinear state space reconstruction: Application to the Great Salt Lake time series. Water Resources Research, 41(12).
  • Khalil, A. F., McKee, M., Kemblowski, M., Asefa, T. & Bastidas, L. (2006). Multiobjective analysis of chaotic dynamic systems with sparse learning machines. Advances in Water Resources, 29(1), 72-88.
  • Ma, X., Zhang, Y. & Wang, Y. (2015). Performance evaluation of kernel functions based on grid search for support vector regression. In 2015 IEEE 7th international conference on cybernetics and intelligent systems (CIS) and IEEE conference on robotics, automation and mechatronics (RAM). IEEE.
  • Pelikan, M., Goldberg, D. E. & Cantú-Paz, E. (2000). Hierarchical Problem Solving and the Bayesian Optimization Algorithm. In GECCO. 267-274.
  • Moriasi, D. N., Gitau, M. W., Pai, N. & Daggupati, P. (2015). Hydrologic and Water Quality Models: Performance Measures and Evaluation Criteria. Transactions of the ASABE, 58(6), 1763-1785.
  • Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106(D7), 7183-7192.

Estimation of Solar Radiation in Adıyaman and Gaziantep Stations Using Support Vector Machines

Yıl 2021, Cilt: 8 Sayı: 2, 753 - 769, 31.12.2021
https://doi.org/10.35193/bseufbd.904393

Öz

Using of solar energy technologies have been developing and increasing in many countries in recent years to meet the energy demand and to provide sustainable energy source. In order to use the developing technologies efficiently, it is necessary to obtain energy source data, and this will ensure that the investments made in this way are more effective. Due to the insufficiency of solar energy measurement devices and the replacement or repair costs of equipment, it is necessary to use the data obtained from meteorological stations as input parameters in the literature and to calculate the solar adiation data with artificial intelligence methods. In this study, the monthly mean solar radiations of Adıyaman and Gaziantep stations are predicted using different input parameters, such as temperature, humidity, mean pressure, wind, number of clear days in a month, and month number. Three different kernel functions (Gaussian, Linear and Polynomial) of support vector machine are used to estimate the average monthly solar radiation. Coefficient of Determination (R2), Root Mean Square Error (RMSE) Mean Absolute Percentage Error (MAPE) Nash–Sutcliffe model efficiency coefficient (NSE) and Percent Bias (PBIAS) parameters are used to determine the performance of selected kernel functions. As a result of the study, it is seen that the Gaussian and Polynomial kernel functions of support vector machines generally show successful output to determine the solar radiation. In addition, it is determined that the models that used mean temperature and mean pressure as input parameters improve the estimation performance.

Kaynakça

  • Senkal, O. & Kuleli, T. (2009). Estimation of solar radiation over Turkey using artificial neural network and satellite data. Applied Energy, 86(7-8), 1222-1228.
  • Badescu, V. (2014). Modeling solar radiation at the earth's surface. Berlin: Springer. 1, 517.
  • Droogers, P. & Allen, R. G. (2002). Estimating Reference Evapotranspiration Under Inaccurate Data Conditions. Irrigation and Drainage Systems, 16(1), 33-45.
  • Mellit, A. (2008). Artificial Intelligence technique for modelling and forecasting of solar radiation data: a review. International Journal of Artificial intelligence and soft computing, 1(1), 52-76.
  • Besharat, F., Dehghan, A. A. & Faghih, A. R. (2013). Empirical models for estimating global solar radiation: A review and case study. Renewable & Sustainable Energy Reviews, 21, 798-821.
  • Guermoui, M., Abdelaziz, R., Gairaa, K., Djemoui, L. & Benkaciali, S. (2020). New temperature-based predicting model for global solar radiation using support vector regression. International Journal of Ambient Energy, 1-11.
  • Mohsenzadeh Karimi, S., Kisi, O., Porrajabali, M., Rouhani-Nia, F. & Shiri, J. (2020). Evaluation of the support vector machine, random forest and geo-statistical methodologies for predicting long-term air temperature. ISH Journal of Hydraulic Engineering, 26(4), 376-386.
  • Fan, J., Wang, X., Wu, L., Zhou, H., Zhang, F., Yu, X., Lu, X. & Xiang, Y. (2018). Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China. Energy Conversion And Management, 164, 102-111.
  • Shiri, J., Kisi, O., Landeras, G., Lopez, J. J., Nazemi, A. H. & Stuyt, L. C. P. M. (2012). Daily reference evapotranspiration modeling by using genetic programming approach in the Basque Country (Northern Spain). Journal of Hydrology, 414, 302-316.
  • Landeras, G., Lopez, J. J., Kisi, O. & Shiri, J. (2012). Comparison of Gene Expression Programming with neuro-fuzzy and neural network computing techniques in estimating daily incoming solar radiation in the Basque Country (Northern Spain). Energy Conversion and Management, 62, 1-13.
  • Güçlü, Y. S., Yeleğen, M. Ö., Dabanlı, İ. & Şişman, E. (2014). Solar irradiation estimations and comparisons by ANFIS, Angström–Prescott and dependency models. Solar Energy, 109, 118-124.
  • Güçlü, Y. S., Dabanlı, İ. & Şişman, E. (2014). Short-and long-term solar radiation estimation method, In Progress in Exergy, Energy, and the Environment. 527-532.
  • Citakoglu, H. (2015). Comparison of artificial intelligence techniques via empirical equations for prediction of solar radiation. Computers and Electronics in Agriculture, 118, 28-37.
  • Güçlü, Y. S., Dabanlı, İ., Şişman, E. & Şen, Z. (2015). HARmonic–LINear (HarLin) model for solar irradiation estimation. Renewable Energy, 81, 209-218.
  • Belaid, S. & Mellit, A. (2016). Prediction of daily and mean monthly global solar radiation using support vector machine in an arid climate. Energy Conversion and Management, 118, 105-118.
  • Chiteka, K. & Enweremadu, C. C. (2016). Prediction of global horizontal solar irradiance in Zimbabwe using artificial neural networks. Journal of Cleaner Production, 135, 701-711.
  • Bakhashwain, J. M. (2016). Prediction of global solar radiation using support vector machines. International Journal of Green Energy, 13(14), 1467-1472.
  • Hassan, G. E., Youssef, M. E., Mohamed, Z. E., Ali, M. A. & Hanafy, A. A. (2016). New Temperature-based Models for Predicting Global Solar Radiation. Applied Energy, 179, 437-450.
  • Laidi, M., Hanini, S., Rezrazi, A., Yaiche, M. R., El Hadj, A. A. & Chellali, F. (2017). Supervised artificial neural network-based method for conversion of solar radiation data (case study: Algeria). Theoretical and Applied Climatology, 128(1-2), 439-451.
  • Wang, L. C., Kisi, O., Zounemat-Kermani, M., Zhu, Z. M., Gong, W., Niu, Z. G., Liu, H. F. & Liu, Z. J. (2017). Prediction of solar radiation in China using different adaptive neuro-fuzzy methods and M5 model tree. International Journal of Climatology, 37(3), 1141-1155.
  • Hassan, M. A., Khalil, A., Kaseb, S. & Kassem, M. A. (2017). Exploring the potential of tree-based ensemble methods in solar radiation modeling. Applied Energy, 203, 897-916.
  • Basaran, K., Ozcift, A. & Kilinc, D. (2019). A New Approach for Prediction of Solar Radiation with Using Ensemble Learning Algorithm. Arabian Journal for Science and Engineering, 44(8), 7159-7171.
  • Gülşen, K., Sönmez, M. E. & Karabaş, M. (2019). Gaziantep İlinde Güneş Enerjisi Potansiyelinin Analitik Hiyerarşi Süreci Yöntemi (AHP) İle Belirlenmesi. Coğrafya Dergisi, (39), 61-72.
  • Alizamir, M., Kim, S., Kisi, O. & Zounemat-Kermani, M. (2020). A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions. Energy, 197.
  • Muhammed, O. (2020). Türkiye’nin Güneş Enerjisi Potansiyeli ve PV Uygulamalarının Yerel Ölçekte Değerlendirilmesi: Karabük İli Örneği. lnternational Journal of Geography and Geography Education, (42), 482-503.
  • Bilir, L. & Yildirim, N. (2018). Modeling and performance analysis of a hybrid system for a residential application. Energy, 163, 555-569.
  • GEPA. (2020). https://gepa.enerji.gov.tr/MyCalculator/ (Erişim tarihi: 26.03.2021)
  • Vapnik, V. (2013). The nature of statistical learning theory. Springer science & business media.
  • Dibike, Y. B., Velickov, S., Solomatine, D. & Abbott, M. B. (2001). Model induction with support vector machines: Introduction and applications. Journal of Computing in Civil Engineering, 15(3), 208-216.
  • Bray, M. & Han, D. (2004). Identification of support vector machines for runoff modelling. Journal of Hydroinformatics, 6(4), 265-280.
  • Chen, H., Guo, J., Xiong, W., Guo, S. L. & Xu, C. Y. (2010). Downscaling GCMs using the Smooth Support Vector Machine method to predict daily precipitation in the Hanjiang Basin. Advances in Atmospheric Sciences, 27(2), 274-284.
  • Nieto, P. J. G., Torres, J. M., Fernandez, M. A. & Galan, C. O. (2012). Support vector machines and neural networks used to evaluate paper manufactured using Eucalyptus globulus. Applied Mathematical Modelling, 36(12), 6137-6145.
  • Hosseini, S. M. & Mahjouri, N. (2016). Integrating Support Vector Regression and a geomorphologic Artificial Neural Network for daily rainfall-runoff modeling. Applied Soft Computing, 38, 329-345.
  • Khan, M. S. & Coulibaly, P. (2006). Application of support vector machine in lake water level prediction. Journal of Hydrologic Engineering, 11(3), 199-205.
  • Asefa, T., Kemblowski, M., Lall, U. & Urroz, G. (2005). Support vector machines for nonlinear state space reconstruction: Application to the Great Salt Lake time series. Water Resources Research, 41(12).
  • Khalil, A. F., McKee, M., Kemblowski, M., Asefa, T. & Bastidas, L. (2006). Multiobjective analysis of chaotic dynamic systems with sparse learning machines. Advances in Water Resources, 29(1), 72-88.
  • Ma, X., Zhang, Y. & Wang, Y. (2015). Performance evaluation of kernel functions based on grid search for support vector regression. In 2015 IEEE 7th international conference on cybernetics and intelligent systems (CIS) and IEEE conference on robotics, automation and mechatronics (RAM). IEEE.
  • Pelikan, M., Goldberg, D. E. & Cantú-Paz, E. (2000). Hierarchical Problem Solving and the Bayesian Optimization Algorithm. In GECCO. 267-274.
  • Moriasi, D. N., Gitau, M. W., Pai, N. & Daggupati, P. (2015). Hydrologic and Water Quality Models: Performance Measures and Evaluation Criteria. Transactions of the ASABE, 58(6), 1763-1785.
  • Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106(D7), 7183-7192.
Toplam 40 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Latif Doğan Dinsever 0000-0001-8573-1539

Veysel Gümüş 0000-0003-2321-9526

Oğuz Şimşek 0000-0001-6324-0229

Yavuz Avşaroğlu 0000-0003-0920-3202

Mehmet Kuş 0000-0003-2215-9250

Yayımlanma Tarihi 31 Aralık 2021
Gönderilme Tarihi 27 Mart 2021
Kabul Tarihi 26 Ağustos 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 8 Sayı: 2

Kaynak Göster

APA Dinsever, L. D., Gümüş, V., Şimşek, O., Avşaroğlu, Y., vd. (2021). Adıyaman ve Gaziantep İstasyonlarındaki Güneşlenme Şiddetinin Destek Vektör Makineleri ile Tahmini. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 8(2), 753-769. https://doi.org/10.35193/bseufbd.904393