Research Article
BibTex RIS Cite

METEOROLOJİK VERİLER KULLANILARAK GÜNEŞ IŞINIM TAHMİNİNDE YAPAY SİNİR AĞLARI PARAMETRELERİNİN DEĞERLENDİRİLMESİ

Year 2022, , 746 - 759, 03.12.2022

Erşan Ömer Yüzer , Altuğ Bozkurt

https://doi.org/10.17780/ksujes.1163446

Abstract

Doğru ışınım tahmini, fotovoltaik (PV) santralinin verimliliğini arttırarak şebekenin etkin bir şekilde programlanmasına ve güç kalitesinin iyileştirilmesine olanak sağlar. Bu çalışma, güneş enerjisi bakımından verimli bir yer olan Hakkâri ilinde kurulan bir meteoroloji ölçüm istasyonu verileri aracılığıyla küresel güneş ışınım tahmininde yapay sinir ağları (YSA) parametrelerinin potansiyelini göstermektedir. Meteoroloji istasyonundan zaman serisine bağlı olarak ölçülen, rüzgâr hızı, sıcaklık, basınç ve nem parametreleri kullanılarak eş zamanlı gerçekleşen güneş ışınım değerleri YSA modeli oluşturularak tahmin edilmiştir. Oluşturulan model YSA’da yaygın olarak kullanılan çeşitli eğitim algoritmaları ve aktivasyon fonksiyonları ile denenmiş ve en iyi sonuç elde edilmeye çalışılmıştır. Kullanılan modelin performansı istatistiksel göstergeler kullanılarak değerlendirilmiştir. Kullanılan veri seti parametrelerine göre güneş ışınım tahmininde, “trainlm” eğitim algoritması ile “poslin” aktivasyon fonksiyonu kullanılarak oluşturulan model 0,97 regresyon değeri, %1,16 ortalama kare hatası (MSE) ve %0,0881 normalize kök ortalama kare hatası (nRMSE) değeri ile güneş ışınım tahmininde en iyi performansı göstermiştir.

Keywords

Güneş ışınım tahmini güneş enerjisi Hakkâri yapay sinir ağları meteorolojik veriler

References

  • Aguiar, L. M., Pereira, B., Lauret, P., Díaz, F., & David, M. (2016). Combining solar irradiance measurements, satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting. Renewable Energy, 97, 599-610.
  • AlSkaif, T., Dev, S., Visser, L., Hossari, M., & van Sark, W. (2020). “A systematic analysis of meteorological variables for PV output power estimation” Renewable Energy, 153, 12-22.
  • Arthur, C. K., Temeng, V. A., & Ziggah, Y. Y. (2020). “Performance evaluation of training algorithms in backpropagation neural network approach to blast-induced ground vibration prediction, Ghana Mining Journal, 20(1), 20-33.
  • Ağbulut, Ü., Gürel, A. E., & Biçen, Y. (2021). “Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison” Renewable and Sustainable Energy Reviews, 135, 110114.
  • Bamisile, O., Oluwasanmi, A., Ejiyi, C., Yimen, N., Obiora, S., & Huang, Q. (2022). “Comparison of machine learning and deep learning algorithms for hourly global/diffuse solar radiation predictions” International Journal of Energy Research, 46(8), 10052-10073.
  • Cornaro, C., Bucci, F., Pierro, M., Del Frate, F., Peronaci, S., & Taravat, A. (2015). Twenty-Four Hour Solar Irradiance Forecast Based on Neural Networks and Numerical Weather Prediction. Journal of Solar Energy Engineering, 137(3).
  • Faisal, A. F., Rahman, A., Habib, M. T. M., Siddique, A. H., Hasan, M., & Khan, M. M. (2022). “Neural networks based multivariate time series forecasting of solar radiation using meteorological data of different cities of Bangladesh” Results in Engineering, 13, 100365.
  • Gala, Y., Fernández, Á., Díaz, J., & Dorronsoro, J. R. (2016). Hybrid machine learning forecasting of solar radiation values. Neurocomputing, 176, 48-59.
  • Gairaa, K., Khellaf, A., Messlem, Y., & Chellali, F. (2016). “Estimation of the daily global solar radiation based on Box–Jenkins and ANN models: A combined approach” Renewable and Sustainable Energy Reviews, 57, 238-249.
  • Gao, B., Huang, X., Shi, J., Tai, Y., & Zhang, J. (2020). Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks. Renewable Energy, 162, 1665-1683.
  • 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.
  • Huang, X., Li, Q., Tai, Y., Chen, Z., Zhang, J., Shi, J., & Liu, W. (2021). Hybrid deep neural model for hourly solar irradiance forecasting. Renewable Energy, 171, 1041-1060.
  • IRENA (2021), Renewable Energy Statistics 2021. The International Renewable Energy Agency, Abu Dhabi.
  • Joshi, B., Kay, M., Copper, J. K., & Sproul, A. B. (2019). Evaluation of solar irradiance forecasting skills of the Australian Bureau of Meteorology’s ACCESS models. Solar Energy, 188, 386-402.
  • Kumar, S., & Kaur, T. (2016). “Development of ANN based model for solar potential assessment using various meteorological parameters” Energy Procedia, 90, 587-592.
  • Khosravi, A., Koury, R. N. N., Machado, L., & Pabon, J. J. G. (2018). “Prediction of hourly solar radiation in Abu Musa Island using machine learning algorithms” Journal of Cleaner Production, 176, 63-75.
  • Karaman, Ö. A., Ağır, T. T., & Arsel, İ. (2021). “Estimation of solar radiation using modern methods” Alexandria Engineering Journal, 60(2), 2447-2455.
  • Lu, N., Qin, J., Yang, K., & Sun, J. (2011). A simple and efficient algorithm to estimate daily global solar radiation from geostationary satellite data. Energy, 36(5), 3179-3188.
  • Lima, F. J., Martins, F. R., Pereira, E. B., Lorenz, E., & Heinemann, D. (2016). Forecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networks. Renewable Energy, 87, 807-818.
  • Molina, A., Falvey, M., & Rondanelli, R. (2017). A solar radiation database for Chile. Scientific reports, 7(1), 1-11.
  • Meenal, R., & Selvakumar, A. I. (2018). Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters. Renewable Energy, 121, 324-343.
  • Marzouq, M., Bounoua, Z., El Fadili, H., Mechaqrane, A., Zenkouar, K., & Lakhliai, Z. (2019). “New daily global solar irradiation estimation model based on automatic selection of input parameters using evolutionary artificial neural networks” Journal of Cleaner Production, 209, 1105-1118.
  • Othman, A. B., Belkilani, K., & Besbes, M. (2020). Prediction improvement of potential PV production pattern, imagery satellite-based. Scientific Reports, 10(1), 1-10.
  • Premalatha, M., & Naveen, C. (2018). “Analysis of different combinations of meteorological parameters in predicting the horizontal global solar radiation with ANN approach: A case study” Renewable and Sustainable Energy Reviews, 91, 248-258.
  • Perveen, G., Rizwan, M., & Goel, N. (2019). Comparison of intelligent modelling techniques for forecasting solar energy and its application in solar PV based energy system. IET Energy Systems Integration, 1(1), 34-51.
  • Premalatha, N., & Valan Arasu, A. (2020). “Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms” Journal of applied research and technology, 14(3), 206-214.
  • Qazi, A., Fayaz, H., Wadi, A., Raj, R. G., Rahim, N. A., & Khan, W. A. (2015). “The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review” Journal of cleaner production, 104, 1-12.
  • Sobri, S., Koohi-Kamali, S., & Rahim, N. A. (2018). Solar photovoltaic generation forecasting methods: A review. Energy Conversion and Management, 156, 459-497.
  • Tuohy, A., Zack, J., Haupt, S. E., Sharp, J., Ahlstrom, M., Dise, S., & Collier, C. (2015). Solar forecasting: methods, challenges, and performance. IEEE Power and Energy Magazine, 13(6), 50-59.
  • Vakili, M., Sabbagh-Yazdi, S. R., Khosrojerdi, S., & Kalhor, K. (2017). “Evaluating the effect of particulate matter pollution on estimation of daily global solar radiation using artificial neural network modeling based on meteorological data” Journal of cleaner production, 141, 1275-1285.
  • Voyant, C., Notton, G., Kalogirou, S., Nivet, M. L., Paoli, C., Motte, F., & Fouilloy, A. (2017). Machine learning methods for solar radiation forecasting: A review. Renewable Energy, 105, 569-582.
  • Wang, H., Cai, R., Zhou, B., Aziz, S., Qin, B., Voropai, N., & Barakhtenko, E. (2020). Solar irradiance forecasting based on direct explainable neural network. Energy Conversion and Management, 226, 113487.
  • Yadav, A. K., & Chandel, S. S. (2014). Solar radiation prediction using Artificial Neural Network techniques: A review. Renewable and sustainable energy reviews, 33, 772-781.
  • Yadav, A. K., Malik, H., & Chandel, S. S. (2014). “Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models” Renewable and Sustainable Energy Reviews, 31, 509-519.
  • Yang, D., Kleissl, J., Gueymard, C. A., Pedro, H. T., & Coimbra, C. F. (2018). “History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining” Solar Energy, 168, 60-101.
  • Zeng, P., Sun, X., & Farnham, D. J. (2020). Skillful statistical models to predict seasonal wind speed and solar radiation in a Yangtze River estuary case study. Scientific reports, 10(1), 1-11.

EVALUATİON OF ARTIFICIAL NEURAL NETWORK PARAMETERS IN SOLAR RADIATION PREDICTION USING METEOROLOGICAL DATA

Year 2022, , 746 - 759, 03.12.2022

Erşan Ömer Yüzer , Altuğ Bozkurt

https://doi.org/10.17780/ksujes.1163446

Abstract

Accurate radiation prediction increases photovoltaic (PV) plant efficiency so ensures effective programming of the grid and improvement of power quality. This study demonstrates the prediction potential of artificial neural networks (ANN) parameters in global solar radiation through data from a meteorological measurement station established in Hakkari, Turkey, which is a solar-efficient place. The occurring simultaneous solar radiation values were estimated using the wind speed, temperature, pressure and humidity parameters obtained from the meteorology station depending on the time series, and the relationships between these parameters were modeled using ANN. The created model was tested with various training algorithms and activation functions, and the best result was tried to be obtained. The performance of this model was evaluated using statistical indicators. In prediction of solar radiation according to used data set parameters, the model established by using “trainlm” training algorithm and “poslin” activation function showed the best performance in solar radiation prediction with 0.97 regression value, 1.16% mean square error (MSE) and 0.0881% normalized root mean square error (nRMSE).

Keywords

Solar radiation prediction solar energy Hakkari artificial neural networks meteorological data

References

  • Aguiar, L. M., Pereira, B., Lauret, P., Díaz, F., & David, M. (2016). Combining solar irradiance measurements, satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting. Renewable Energy, 97, 599-610.
  • AlSkaif, T., Dev, S., Visser, L., Hossari, M., & van Sark, W. (2020). “A systematic analysis of meteorological variables for PV output power estimation” Renewable Energy, 153, 12-22.
  • Arthur, C. K., Temeng, V. A., & Ziggah, Y. Y. (2020). “Performance evaluation of training algorithms in backpropagation neural network approach to blast-induced ground vibration prediction, Ghana Mining Journal, 20(1), 20-33.
  • Ağbulut, Ü., Gürel, A. E., & Biçen, Y. (2021). “Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison” Renewable and Sustainable Energy Reviews, 135, 110114.
  • Bamisile, O., Oluwasanmi, A., Ejiyi, C., Yimen, N., Obiora, S., & Huang, Q. (2022). “Comparison of machine learning and deep learning algorithms for hourly global/diffuse solar radiation predictions” International Journal of Energy Research, 46(8), 10052-10073.
  • Cornaro, C., Bucci, F., Pierro, M., Del Frate, F., Peronaci, S., & Taravat, A. (2015). Twenty-Four Hour Solar Irradiance Forecast Based on Neural Networks and Numerical Weather Prediction. Journal of Solar Energy Engineering, 137(3).
  • Faisal, A. F., Rahman, A., Habib, M. T. M., Siddique, A. H., Hasan, M., & Khan, M. M. (2022). “Neural networks based multivariate time series forecasting of solar radiation using meteorological data of different cities of Bangladesh” Results in Engineering, 13, 100365.
  • Gala, Y., Fernández, Á., Díaz, J., & Dorronsoro, J. R. (2016). Hybrid machine learning forecasting of solar radiation values. Neurocomputing, 176, 48-59.
  • Gairaa, K., Khellaf, A., Messlem, Y., & Chellali, F. (2016). “Estimation of the daily global solar radiation based on Box–Jenkins and ANN models: A combined approach” Renewable and Sustainable Energy Reviews, 57, 238-249.
  • Gao, B., Huang, X., Shi, J., Tai, Y., & Zhang, J. (2020). Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks. Renewable Energy, 162, 1665-1683.
  • 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.
  • Huang, X., Li, Q., Tai, Y., Chen, Z., Zhang, J., Shi, J., & Liu, W. (2021). Hybrid deep neural model for hourly solar irradiance forecasting. Renewable Energy, 171, 1041-1060.
  • IRENA (2021), Renewable Energy Statistics 2021. The International Renewable Energy Agency, Abu Dhabi.
  • Joshi, B., Kay, M., Copper, J. K., & Sproul, A. B. (2019). Evaluation of solar irradiance forecasting skills of the Australian Bureau of Meteorology’s ACCESS models. Solar Energy, 188, 386-402.
  • Kumar, S., & Kaur, T. (2016). “Development of ANN based model for solar potential assessment using various meteorological parameters” Energy Procedia, 90, 587-592.
  • Khosravi, A., Koury, R. N. N., Machado, L., & Pabon, J. J. G. (2018). “Prediction of hourly solar radiation in Abu Musa Island using machine learning algorithms” Journal of Cleaner Production, 176, 63-75.
  • Karaman, Ö. A., Ağır, T. T., & Arsel, İ. (2021). “Estimation of solar radiation using modern methods” Alexandria Engineering Journal, 60(2), 2447-2455.
  • Lu, N., Qin, J., Yang, K., & Sun, J. (2011). A simple and efficient algorithm to estimate daily global solar radiation from geostationary satellite data. Energy, 36(5), 3179-3188.
  • Lima, F. J., Martins, F. R., Pereira, E. B., Lorenz, E., & Heinemann, D. (2016). Forecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networks. Renewable Energy, 87, 807-818.
  • Molina, A., Falvey, M., & Rondanelli, R. (2017). A solar radiation database for Chile. Scientific reports, 7(1), 1-11.
  • Meenal, R., & Selvakumar, A. I. (2018). Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters. Renewable Energy, 121, 324-343.
  • Marzouq, M., Bounoua, Z., El Fadili, H., Mechaqrane, A., Zenkouar, K., & Lakhliai, Z. (2019). “New daily global solar irradiation estimation model based on automatic selection of input parameters using evolutionary artificial neural networks” Journal of Cleaner Production, 209, 1105-1118.
  • Othman, A. B., Belkilani, K., & Besbes, M. (2020). Prediction improvement of potential PV production pattern, imagery satellite-based. Scientific Reports, 10(1), 1-10.
  • Premalatha, M., & Naveen, C. (2018). “Analysis of different combinations of meteorological parameters in predicting the horizontal global solar radiation with ANN approach: A case study” Renewable and Sustainable Energy Reviews, 91, 248-258.
  • Perveen, G., Rizwan, M., & Goel, N. (2019). Comparison of intelligent modelling techniques for forecasting solar energy and its application in solar PV based energy system. IET Energy Systems Integration, 1(1), 34-51.
  • Premalatha, N., & Valan Arasu, A. (2020). “Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms” Journal of applied research and technology, 14(3), 206-214.
  • Qazi, A., Fayaz, H., Wadi, A., Raj, R. G., Rahim, N. A., & Khan, W. A. (2015). “The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review” Journal of cleaner production, 104, 1-12.
  • Sobri, S., Koohi-Kamali, S., & Rahim, N. A. (2018). Solar photovoltaic generation forecasting methods: A review. Energy Conversion and Management, 156, 459-497.
  • Tuohy, A., Zack, J., Haupt, S. E., Sharp, J., Ahlstrom, M., Dise, S., & Collier, C. (2015). Solar forecasting: methods, challenges, and performance. IEEE Power and Energy Magazine, 13(6), 50-59.
  • Vakili, M., Sabbagh-Yazdi, S. R., Khosrojerdi, S., & Kalhor, K. (2017). “Evaluating the effect of particulate matter pollution on estimation of daily global solar radiation using artificial neural network modeling based on meteorological data” Journal of cleaner production, 141, 1275-1285.
  • Voyant, C., Notton, G., Kalogirou, S., Nivet, M. L., Paoli, C., Motte, F., & Fouilloy, A. (2017). Machine learning methods for solar radiation forecasting: A review. Renewable Energy, 105, 569-582.
  • Wang, H., Cai, R., Zhou, B., Aziz, S., Qin, B., Voropai, N., & Barakhtenko, E. (2020). Solar irradiance forecasting based on direct explainable neural network. Energy Conversion and Management, 226, 113487.
  • Yadav, A. K., & Chandel, S. S. (2014). Solar radiation prediction using Artificial Neural Network techniques: A review. Renewable and sustainable energy reviews, 33, 772-781.
  • Yadav, A. K., Malik, H., & Chandel, S. S. (2014). “Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models” Renewable and Sustainable Energy Reviews, 31, 509-519.
  • Yang, D., Kleissl, J., Gueymard, C. A., Pedro, H. T., & Coimbra, C. F. (2018). “History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining” Solar Energy, 168, 60-101.
  • Zeng, P., Sun, X., & Farnham, D. J. (2020). Skillful statistical models to predict seasonal wind speed and solar radiation in a Yangtze River estuary case study. Scientific reports, 10(1), 1-11.
There are 36 citations in total.

Details

Primary Language Turkish
Subjects Electrical Engineering
Journal Section Electrical and Electronics Engineering
Authors

Erşan Ömer Yüzer 0000-0002-9089-1358

Altuğ Bozkurt 0000-0001-6458-1260

Publication Date December 3, 2022
Submission Date August 17, 2022
Published in Issue Year 2022

Cite

APA Yüzer, E. Ö., & Bozkurt, A. (2022). METEOROLOJİK VERİLER KULLANILARAK GÜNEŞ IŞINIM TAHMİNİNDE YAPAY SİNİR AĞLARI PARAMETRELERİNİN DEĞERLENDİRİLMESİ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 25(4), 746-759. https://doi.org/10.17780/ksujes.1163446