Öz
This study will enable us to estimate the power of the solar power plant with measurement data such as outdoor temperature-humidity, wind and precipitation amount, to protect the system from imbalance, and to determine the instant and daily effective energy trade more easily. By taking the data from the solar power plant installed in Samsun, Turkey, estimation was made with Artificial Neural Networks for electricity generation. In this study, Levenberg-Marguardt feed-forward backprop learning algorithm was used to find the best approach in the network. The best prediction results were obtained from the 2-layer and 5-neuron Artificial Neural Networks model, and it was observed that the system gave better training results as the number of iterations increased (multiple determination coefficient, R2, 0.99818).
Anahtar Kelimeler
Artificial neural network Solar Energy Power plant Electricity generation
Kaynakça
- Cengiz, C. (2023). Comparison of the parameters of solar power plants with the same characteristics installed in four different directions. (Master’s Dissertation, Fırat University).
- International Energy Agency (2023). Solar PV Global Supply Chains – Analysis 2023. Retrieved March 2, 2023 from https://www.iea.org/reports/solar-pv-global-supply-chains/executive-summary
- Sahin, H., Esen, H. (2022). The usage of renewable energy sources and its effects on GHG emission intensity of electricity generation in Turkey. Renew Energy 192:859–69.
- Republic of Turkey Ministry of Energy and Natural Resources. Electricity (2023). Retrieved March 2, 2023 from https://enerji.gov.tr/infobank-energy-electricity
- Alvara, R.R. (2018). Design and Modelling of a Large-Scale PV Plant. Escola Tècnica Superior d’Enginyeria Industrial de Barcelona.
- Gök, A. O., Yıldız, C., & Şekkeli, M. (2019). Yapay sinir ağları kullanarak kısa dönem güneş enerjisi santrali üretim tahmini: Kahramanmaraş örnek çalışması. Uluslararası Doğu Anadolu Fen Mühendislik ve Tasarım Dergisi, 1(2), 186-195.
- İçel Y. (2019). Güneş enerji sistemlerinin performans tahmini için yapay sinir ağları ile modellenmesi ve verimliliğin incelenmesi (Master’s Dissertation, Inonu University)
- Dinçer, A., & İlhan, U. (2022). Fotovoltaik Panellerde Güç Tahminlenmesi için Yapay Zekâ Yöntemlerinin Kullanılması. Tekirdağ Ziraat Fakültesi Dergisi, 19(2), 435-445.
- Nkuriyingoma, O., & Selçuklu S. B. (2021). Solar power plant generation forecasting using NARX neural network model: A case study, I. J. of Energy Applications and Technologies, 8(3), 80-92.
- Wang, F., Mi, Z., Su, S., & Zhao, H. (2012). Short-term solar irradiance forecasting model based on artificial neural network using statistical feature parameters. Energies, 5, 1355–1370.
- Mellit, A., & Pavan, A. M. (2010). A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy. Sol. Energy, 84, 807–821.
- Hocaoglu, F. O., Gerek, O. N., & Kurban, M. (2008). Hourly solar radiation forecasting using optimal coefficient 2-D linear filters and feed-forward neural networks. Sol. Energy, 82, 714–726.
- Pedro, H. T. C., & Coimbra, C. F. M. (2012). Assessment of forecasting techniques for solar power production with no exogenous inputs. Sol. Energy, 86, 2017–2028.
- Izgi, E., Öztopal, A., Yerli, B., Kaymak, M. K., & Sahin, A. D. (2012). Short-mid-term solar power prediction by using artificial neural networks. Sol. Energy, 86, 725–733.
- Shi, J., Lee, W.J., Liu, Y., Yang, Y., & Wang, P. (2012). Forecasting power output of photovoltaic systems based on weather classification and support vector machines. IEEE Trans. Ind. Appl., 48, 1064–1069.
- Alamsyah, T., Sopian, K., & Shahrir, A. (2004). Predicting average energy conversion of photovoltaic system in Malaysia using a simplified method. Renew. Energy, 29, 403–411.
- Monteiro, C., Fernandez-Jimenez, L. A., Ramirez-Rosadoc, I.J., Muñoz-Jimenez, A., & Lara-Santillan, P. M. (2013). Short-term forecasting models for photovoltaic plants: Analytical versus soft-computing techniques. Math. Problems Eng.
- Ulbricht, R., Fischer, U., Lehner, W., & Donker, H. ( 23–27 September 2013). First steps towards a systematical optimized strategy for solar energy supply forecasting. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2013), Prague, Czech Republic.
- Ogliari, E., Grimaccia, F., Leva, S., & Mussetta, M. (2013). Hybrid predictive models for accurate forecasting in PV systems. Energies, 6, 1918–1929.
- Dolara, A., Grimaccia, F., Leva, S., Mussetta, M., & Ogliari, E. (2015). A physical hybrid artificial neural network for short term forecasting of PV plant power output, Energies, 8:1138–53.
- Abdelhak K., Razika I., Ali B., Abdelmalek A., Müslüm A., Nacer L., & Nabila I. (2023). Solar photovoltaic power prediction using artificial neural network and multiple regression considering ambient and operating conditions, Energy Conversion and Management, Volume 288.
- Rocha Vaz, A.G.C. (2014). Photovoltaic Forecasting with Artificial Neural Networks, (Master’s Dissertation, Lisboa University.)
Öz
Bu çalışma, dış ortam sıcaklığı-nemi, rüzgar ve yağış miktarı gibi ölçüm verileriyle güneş enerjisi santralinin gücünü tahmin etmemizi, sistemi dengesizlikten korumamızı, anlık ve günlük efektif enerji ticaretini daha kolay belirlememizi sağlayacaktır. Samsun'da kurulu güneş enerjisi santralinden veriler alınarak elektrik üretimi için Yapay Sinir Ağları ile tahmin yapıldı. Bu çalışmada ağdaki en iyi yaklaşımı bulmak için Levenberg-Marguardt ileri beslemeli backprop öğrenme algoritması kullanılmıştır. En iyi tahmin sonuçları 2 katmanlı ve 5 nöronlu Yapay Sinir Ağları modelinden elde edilmiş olup, yineleme sayısı arttıkça sis-temin daha iyi eğitim sonuçları verdiği gözlemlenmiştir (çoklu belirleme katsayısı, R2, 0,99818).
Anahtar Kelimeler
Yapay Sinir Ağı Güneş Enerji Enerji Santrali Elektrik Üretimi
Kaynakça
- Cengiz, C. (2023). Comparison of the parameters of solar power plants with the same characteristics installed in four different directions. (Master’s Dissertation, Fırat University).
- International Energy Agency (2023). Solar PV Global Supply Chains – Analysis 2023. Retrieved March 2, 2023 from https://www.iea.org/reports/solar-pv-global-supply-chains/executive-summary
- Sahin, H., Esen, H. (2022). The usage of renewable energy sources and its effects on GHG emission intensity of electricity generation in Turkey. Renew Energy 192:859–69.
- Republic of Turkey Ministry of Energy and Natural Resources. Electricity (2023). Retrieved March 2, 2023 from https://enerji.gov.tr/infobank-energy-electricity
- Alvara, R.R. (2018). Design and Modelling of a Large-Scale PV Plant. Escola Tècnica Superior d’Enginyeria Industrial de Barcelona.
- Gök, A. O., Yıldız, C., & Şekkeli, M. (2019). Yapay sinir ağları kullanarak kısa dönem güneş enerjisi santrali üretim tahmini: Kahramanmaraş örnek çalışması. Uluslararası Doğu Anadolu Fen Mühendislik ve Tasarım Dergisi, 1(2), 186-195.
- İçel Y. (2019). Güneş enerji sistemlerinin performans tahmini için yapay sinir ağları ile modellenmesi ve verimliliğin incelenmesi (Master’s Dissertation, Inonu University)
- Dinçer, A., & İlhan, U. (2022). Fotovoltaik Panellerde Güç Tahminlenmesi için Yapay Zekâ Yöntemlerinin Kullanılması. Tekirdağ Ziraat Fakültesi Dergisi, 19(2), 435-445.
- Nkuriyingoma, O., & Selçuklu S. B. (2021). Solar power plant generation forecasting using NARX neural network model: A case study, I. J. of Energy Applications and Technologies, 8(3), 80-92.
- Wang, F., Mi, Z., Su, S., & Zhao, H. (2012). Short-term solar irradiance forecasting model based on artificial neural network using statistical feature parameters. Energies, 5, 1355–1370.
- Mellit, A., & Pavan, A. M. (2010). A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy. Sol. Energy, 84, 807–821.
- Hocaoglu, F. O., Gerek, O. N., & Kurban, M. (2008). Hourly solar radiation forecasting using optimal coefficient 2-D linear filters and feed-forward neural networks. Sol. Energy, 82, 714–726.
- Pedro, H. T. C., & Coimbra, C. F. M. (2012). Assessment of forecasting techniques for solar power production with no exogenous inputs. Sol. Energy, 86, 2017–2028.
- Izgi, E., Öztopal, A., Yerli, B., Kaymak, M. K., & Sahin, A. D. (2012). Short-mid-term solar power prediction by using artificial neural networks. Sol. Energy, 86, 725–733.
- Shi, J., Lee, W.J., Liu, Y., Yang, Y., & Wang, P. (2012). Forecasting power output of photovoltaic systems based on weather classification and support vector machines. IEEE Trans. Ind. Appl., 48, 1064–1069.
- Alamsyah, T., Sopian, K., & Shahrir, A. (2004). Predicting average energy conversion of photovoltaic system in Malaysia using a simplified method. Renew. Energy, 29, 403–411.
- Monteiro, C., Fernandez-Jimenez, L. A., Ramirez-Rosadoc, I.J., Muñoz-Jimenez, A., & Lara-Santillan, P. M. (2013). Short-term forecasting models for photovoltaic plants: Analytical versus soft-computing techniques. Math. Problems Eng.
- Ulbricht, R., Fischer, U., Lehner, W., & Donker, H. ( 23–27 September 2013). First steps towards a systematical optimized strategy for solar energy supply forecasting. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2013), Prague, Czech Republic.
- Ogliari, E., Grimaccia, F., Leva, S., & Mussetta, M. (2013). Hybrid predictive models for accurate forecasting in PV systems. Energies, 6, 1918–1929.
- Dolara, A., Grimaccia, F., Leva, S., Mussetta, M., & Ogliari, E. (2015). A physical hybrid artificial neural network for short term forecasting of PV plant power output, Energies, 8:1138–53.
- Abdelhak K., Razika I., Ali B., Abdelmalek A., Müslüm A., Nacer L., & Nabila I. (2023). Solar photovoltaic power prediction using artificial neural network and multiple regression considering ambient and operating conditions, Energy Conversion and Management, Volume 288.
- Rocha Vaz, A.G.C. (2014). Photovoltaic Forecasting with Artificial Neural Networks, (Master’s Dissertation, Lisboa University.)
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Enerji |
| Bölüm | Makaleler |
| Yazarlar | |
| Erken Görünüm Tarihi | 29 Aralık 2023 |
| Yayımlanma Tarihi | 31 Aralık 2023 |
| Gönderilme Tarihi | 2 Ağustos 2023 |
| Yayımlandığı Sayı | Yıl 2023 Cilt: 7 Sayı: 2 |
Kaynak Göster
