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Monthly Electricity Generatıon Forecast in Solar Power Plants with LSTM

Year 2021, Volume: 9 Issue: 6 - ICAIAME 2021, 55 - 64, 31.12.2021

Ömer Çetin Ali Hakan Isık

https://doi.org/10.29130/dubited.1015251
Cited By: 2

Abstract

Today, with the intensive use of electrical devices, the need for electricity has increased. Fossil fuels are generally used to meet this need. However, considering the damage caused by fossil fuels to the environment, governments make various incentives for renewable energy sources. The incentives of countries for solar power plants are quite large. Recently, there are many investors who want to build solar power plants. The sunshine duration of our country is quite high. And the fact that the climatic conditions are efficient for the generation of electricity attracts many investors. However, the installation of these power plants is quite costly. It is possible to predict the amortization periods of these costs with the ever-developing artificial intelligence technology. In this study, the energy data to be produced in the future is estimated by using real solar power plant data with machine learning algorithms. Data, take from solar power plants owned by Humartaş Energy company. In the study, predictions and analyses were made using the LSTM (Long Short-Term Memory) method, which is one of the artificial neural networks. The error rate of the study between 1% and 15%. It is foreseen that studies will also be implemented with other renewable energy sources such as wind, geothermal, hydraulic energy data in the coming stages.

Keywords

LSTM Artificial intelligence Solar power plants Energy forecast

References

  • [1] Word Energy Outlook 2013, (2013). World Energy Outlook 2013 [Online]. Available: www.iea.org/reports/world-energy-outlook-2013.
  • [2] T.C. Enerji ve Tabii Kaynaklar Bakanlığı, (2020). Güneş [Çevrimiçi]. Erişim: www.enerji.gov.tr/bilgi-merkezi-enerji-gunes.
  • [3] F. Özen, (2019, Haziran). Yenilenebilir Enerjide Yapay Zeka Uygulamaları [Çevrimiçi]. Erişim: www.researchgate.net/publication/333816757_YENILENEBILIR_ENERJIDE_YAPAY_ZEKA_UYGULAMALARI.
  • [4] T. Boyekin, I. Kıyak, “Rooftop solar power plant based electric vehicle charging station,” in 6. European Conference On Renewable Energy Systems (ECRES), 2018, pp. 959-966.
  • [5] H. Hemza, C. Abdeslam, M. P. Rachid and D. Barakel, “Tracing current-voltage curve of solar panel based on LabVIEW Arduino interfacing,” Bilişim Teknolojileri Dergisi, vol. 8, no. 3, pp. 117–123, 2015.
  • [6] V. Ciocia, A. Boicea, A. Dematteis, P. D. Leo, F. Giordano and F. Spertino, “PV system integration in buildings: an energy and economic case study,” in 2017 10th International Symposium on Advanced Topics in Electrical Engineering (ATEE), 2017, pp. 786-790.
  • [7] P. Grunow, A. Preiss, S. Koch and S. Krauter “Yield and Spectral Effects of A-Si Modules,” in Proceedings of the 24th European Photovoltaic Solar Energy Conference, 2009, pp. 2846-2829.
  • [8] B. Herteleer, J. Cappelle and J. Driesen, “Quantifying low-light behaviour of photovoltaic modules by identifying their irradiance-dependent efficiency from data sheets,” in European Photovoltaic Solar Energy Conference, pp. 1-8, 2012.
  • [9] Solar PV Word Expo, (2021, August). Solar Cells [Online], Available: https://www.pvresources.com/en/solarcells/solarcells.php.
  • [10] E. Koutroulis, F. Blaabjerg, “Methods for the Optimal Design of Grid-Connected PV Inverters, International Journal Of Renewable Energy Research,” IJRER, vol. 1, no. 2, pp. 54-64, 2011.
  • [11] B. Giesler, “String vs. Central inverters: Dimension of the inverter,” in Photon’s 1st PV Inverter Conference, 2010, pp. 1-23.
  • [12] Oğuzhan, (2019). Fotovoltaik Sistemlerin Verimliligin Etkilen Faktorler [Çevrimiçi]. Erişim: www.enerjisistemlerimuhendisligi.com/fotovoltaik-sistemlerin-verimliligin-etkilen-faktorler.html.
  • [13] O. Akköse, (2020, 22 Aralık). Uzun-Kısa Vadeli Bellek (LSTM) [Çevrimiçi]. Erişim: https://medium.com/deep-learning-turkiye/uzun-k%C4%B1sa-vadeli-bellek-lstm-b018c07174a3.
  • [14] A. Gensler, J. Henze, B. Sick and N. Raabe, “Deep Learning for solar power forecasting—An approach using AutoEncoder and LSTM Neural Networks,” in 2016 IEEE İnternational Conference on Systems, Man, and Cybernetics (SMC), 2016, pp. 2858-2865.
  • [15] S. M. Susanti, E. Sulistianingsih, “K Nearest Neighbor dalam Imputasi Missing Data,” Bimaster, vol. 7, no. 1, pp. 9-14, 2018.

LSTM ile Güneş Enerjisi Santrallerinde Aylık Elektrik Üretim Tahmini

Year 2021, Volume: 9 Issue: 6 - ICAIAME 2021, 55 - 64, 31.12.2021

Ömer Çetin Ali Hakan Isık

https://doi.org/10.29130/dubited.1015251
Cited By: 2

Abstract

Günümüzde elektrikli cihazların yoğun kullanımı ile elektriğe olan ihtiyaç artmıştır. Bu ihtiyacı karşılamak için genellikle fosil yakıtlar kullanılmaktadır. Ancak fosil yakıtların çevreye verdiği zararı göz önünde bulundurarak hükümetler yenilenebilir enerji kaynakları için çeşitli teşvikler yapmaktadır. Ülkelerin güneş enerjisi santrallerine yönelik teşvikleri oldukça fazladır. Son zamanlarda güneş enerjisi santrali kurmak isteyen birçok yatırımcı var. Ülkemizin güneşlenme süresi oldukça yüksektir. İklim koşullarının elektrik üretimi için verimli olması da birçok yatırımcıyı cezbetmektedir. Ancak bu santrallerin kurulumu oldukça maliyetlidir. Sürekli gelişen yapay zekâ teknolojisi ile bu maliyetlerin amortisman sürelerini tahmin etmek mümkün. Bu çalışmada, makine öğrenmesi algoritmaları ile gerçek güneş enerjisi santrali verileri kullanılarak gelecekte üretilecek enerji verileri tahmin edilmektedir. Veriler, Humartaş Enerji firmasına ait güneş enerjisi santrallerinden alınmıştır. Çalışmada yapay sinir ağlarından biri olan LSTM (Uzun Kısa Süreli Bellek) yöntemi kullanılarak tahmin ve analizler yapılmıştır. Çalışmanın hata oranı %1 ile %15 arasındadır. Önümüzdeki aşamalarda rüzgâr, jeotermal, hidrolik enerji gibi diğer yenilenebilir enerji kaynakları ile de çalışmaların yapılması öngörülmektedir.

Keywords

LSTM Yapay Zekâ Güneş Enerji Santralleri Enerji Tahmini

References

  • [1] Word Energy Outlook 2013, (2013). World Energy Outlook 2013 [Online]. Available: www.iea.org/reports/world-energy-outlook-2013.
  • [2] T.C. Enerji ve Tabii Kaynaklar Bakanlığı, (2020). Güneş [Çevrimiçi]. Erişim: www.enerji.gov.tr/bilgi-merkezi-enerji-gunes.
  • [3] F. Özen, (2019, Haziran). Yenilenebilir Enerjide Yapay Zeka Uygulamaları [Çevrimiçi]. Erişim: www.researchgate.net/publication/333816757_YENILENEBILIR_ENERJIDE_YAPAY_ZEKA_UYGULAMALARI.
  • [4] T. Boyekin, I. Kıyak, “Rooftop solar power plant based electric vehicle charging station,” in 6. European Conference On Renewable Energy Systems (ECRES), 2018, pp. 959-966.
  • [5] H. Hemza, C. Abdeslam, M. P. Rachid and D. Barakel, “Tracing current-voltage curve of solar panel based on LabVIEW Arduino interfacing,” Bilişim Teknolojileri Dergisi, vol. 8, no. 3, pp. 117–123, 2015.
  • [6] V. Ciocia, A. Boicea, A. Dematteis, P. D. Leo, F. Giordano and F. Spertino, “PV system integration in buildings: an energy and economic case study,” in 2017 10th International Symposium on Advanced Topics in Electrical Engineering (ATEE), 2017, pp. 786-790.
  • [7] P. Grunow, A. Preiss, S. Koch and S. Krauter “Yield and Spectral Effects of A-Si Modules,” in Proceedings of the 24th European Photovoltaic Solar Energy Conference, 2009, pp. 2846-2829.
  • [8] B. Herteleer, J. Cappelle and J. Driesen, “Quantifying low-light behaviour of photovoltaic modules by identifying their irradiance-dependent efficiency from data sheets,” in European Photovoltaic Solar Energy Conference, pp. 1-8, 2012.
  • [9] Solar PV Word Expo, (2021, August). Solar Cells [Online], Available: https://www.pvresources.com/en/solarcells/solarcells.php.
  • [10] E. Koutroulis, F. Blaabjerg, “Methods for the Optimal Design of Grid-Connected PV Inverters, International Journal Of Renewable Energy Research,” IJRER, vol. 1, no. 2, pp. 54-64, 2011.
  • [11] B. Giesler, “String vs. Central inverters: Dimension of the inverter,” in Photon’s 1st PV Inverter Conference, 2010, pp. 1-23.
  • [12] Oğuzhan, (2019). Fotovoltaik Sistemlerin Verimliligin Etkilen Faktorler [Çevrimiçi]. Erişim: www.enerjisistemlerimuhendisligi.com/fotovoltaik-sistemlerin-verimliligin-etkilen-faktorler.html.
  • [13] O. Akköse, (2020, 22 Aralık). Uzun-Kısa Vadeli Bellek (LSTM) [Çevrimiçi]. Erişim: https://medium.com/deep-learning-turkiye/uzun-k%C4%B1sa-vadeli-bellek-lstm-b018c07174a3.
  • [14] A. Gensler, J. Henze, B. Sick and N. Raabe, “Deep Learning for solar power forecasting—An approach using AutoEncoder and LSTM Neural Networks,” in 2016 IEEE İnternational Conference on Systems, Man, and Cybernetics (SMC), 2016, pp. 2858-2865.
  • [15] S. M. Susanti, E. Sulistianingsih, “K Nearest Neighbor dalam Imputasi Missing Data,” Bimaster, vol. 7, no. 1, pp. 9-14, 2018.
There are 15 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Ömer Çetin 0000-0002-2408-3256

Ali Hakan Isık 0000-0003-3561-9375

Publication Date December 31, 2021
Published in Issue Year 2021 Volume: 9 Issue: 6 - ICAIAME 2021

Cite

APA Çetin, Ö., & Isık, A. H. (2021). Monthly Electricity Generatıon Forecast in Solar Power Plants with LSTM. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi, 9(6), 55-64. https://doi.org/10.29130/dubited.1015251
AMA Çetin Ö, Isık AH. Monthly Electricity Generatıon Forecast in Solar Power Plants with LSTM. DUBİTED. December 2021;9(6):55-64. doi:10.29130/dubited.1015251
Chicago Çetin, Ömer, and Ali Hakan Isık. “Monthly Electricity Generatıon Forecast in Solar Power Plants With LSTM”. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi 9, no. 6 (December 2021): 55-64. https://doi.org/10.29130/dubited.1015251.
EndNote Çetin Ö, Isık AH (December 1, 2021) Monthly Electricity Generatıon Forecast in Solar Power Plants with LSTM. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 9 6 55–64.
IEEE Ö. Çetin and A. H. Isık, “Monthly Electricity Generatıon Forecast in Solar Power Plants with LSTM”, DUBİTED, vol. 9, no. 6, pp. 55–64, 2021, doi: 10.29130/dubited.1015251.
ISNAD Çetin, Ömer - Isık, Ali Hakan. “Monthly Electricity Generatıon Forecast in Solar Power Plants With LSTM”. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 9/6 (December 2021), 55-64. https://doi.org/10.29130/dubited.1015251.
JAMA Çetin Ö, Isık AH. Monthly Electricity Generatıon Forecast in Solar Power Plants with LSTM. DUBİTED. 2021;9:55–64.
MLA Çetin, Ömer and Ali Hakan Isık. “Monthly Electricity Generatıon Forecast in Solar Power Plants With LSTM”. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi, vol. 9, no. 6, 2021, pp. 55-64, doi:10.29130/dubited.1015251.
Vancouver Çetin Ö, Isık AH. Monthly Electricity Generatıon Forecast in Solar Power Plants with LSTM. DUBİTED. 2021;9(6):55-64.

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