Aylık Toplam Güneş Işınımının Uzun-Kısa Süreli Bellek (LSTM) Yöntemiyle Tahmini: Sivas İli Örneği

Cahit Gürlek; Mehmet Bilgili

EN TR

Estimation of Monthly Global Solar Radiation Using Long-Short Term Memory (LSTM) Method: A Case Study of Sivas Province

Abstract

Accurate estimation of global solar radiation is critical for solar energy conversion systems (modelling, design and operation) and future investment policies. In this study, daily average monthly solar radiation estimation were performed using the long-short term memory (LSTM) method. For this aim, monthly sunshine radiation data obtained from the Sivas Province in the Central Anatolia Region of Turkey was used. Mean absolute percent error (MAPE), root mean square error (RMSE) and correlation coefficient (R) tests were used for forecast accuracy assessment. The results showed that the LTSM model predicted solar radiation effectively with MAPE of 9.446%, RMSE of 0.496 kWh/m2day, and R of 0.976 for the study area.

Keywords

LSTM , Machine Learning , Monthly Solar Radiation , Sivas

Kaynakça

Akinoğlu, B. G. & Ecevit, A. (1990). Construction of a Quadratic Model Using Modified Angstrom Coefficients to Estimate Global Solar Radiation. Solar Energy, 45 (2), 85–92. https://doi.org/10.1016/0038-092X(90)90032-8
Arslan, G., Bayhan, B. & Yaman, K. (2019). Mersin / Türkiye için Ölçülen Global Güneş Işınımının Yapay Sinir Ağları ile Tahmin Edilmesi ve Yaygın Işınım Modelleri ile Karşılaştırılması. Gazi University Journal of Science Part C: Design and Technology, 7 (1), 80-96. https://doi.org/10.29109/gujsc.419473
Bakirci, K. (2009). Correlations for Estimation of Daily Global Solar Radiation with Hours of Bright Sunshine in Turkey. Energy, 34 (4), 485–501. https://doi.org/10.1016/j.energy.2009.02.005
Ballı, Ö. (2021). Estimating Global Solar Radiation from Empirical Models: An Application. European Mechanical Science, 5 (3), 135-147. https://doi.org/10.26701/ems.797177
Bilgili, M. & Ozgoren, M. (2011). Daily Total Global Solar Radiation Modeling from Several Meteorological Data. Meteorology and Atmospheric Physics, 112, 125-138. https://doi.org/10.1007/s00703-011-0137-9
Bilgili, M., Arslan, N., Şekertekin, A, & Yaşar, A. (2022). Application of Long Short-Term Memory (LSTM) Neural Network Based on Deeplearning for Electricity Energy Consumption Forecasting. Turkish Journal of Electrical Engineering and Computer Sciences, 30 (1), 140-157. https://doi.org/10.3906/elk-2011-14
BP. (2022). Statistical Review of World Energy.
Celik, A. N. & Muneer, T. (2013). Neural Network Based Method for Conversion of Solar Radiation Data. Energy Conversion and Management, 67, 117–124. https://doi.org/10.1016/j.enconman.2012.11.010
Gers, F. A., Schmidhuber, J. & Cummins, F. (2000). Learning to Forget: Continual Prediction with LSTM. Neural Computation, 12 (10), 2451-2471. https://doi.org/10.1162/089976600300015015
Gurlek, C. & Sahin, M. (2018). Estimation of the Global Solar Radiation with the Artificial Neural Networks for the City of Sivas. European Mechanical Science, 2 (2), 46-51. https://doi.org/10.26701/ems.359681

Gül, M. & Çelik, E. (2017). ANFIS Kullanılarak Tunceli İli için Global Güneş Radyasyonu Tahmini. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 8 (4), 891-899.
Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9 (8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Koca, A., Oztop, H. F., Varol, Y. & Koca, G. O. (2011). Estimation of Solar Radiation Using Artificial Neural Networks with Different Input Parameters for Mediterranean Region of Anatolia in Turkey. Expert Systems with Applications, 38 (7), 8756–8762. https://doi.org/10.1016/j.eswa.2011.01.085
Ozgoren, M., Bilgili, M. & Sahin, B. (2012). Estimation of Global Solar Radiation using ANN over Turkey. Expert Systems with Applications, 39 (5), 5043-5051. https://doi.org/10.1016/j.eswa.2011.11.036
Rao, K. D. V. S. K., 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, 248–58. https://doi.org/10.1016/j.rser.2018.03.096
Sahin, M., Kaya, Y. & Uyar, M. (2013). Comparison of ANN and MLR Models for Estimating Solar Radiation in Turkey using NOAA/AVHRR Data. Advances in Space Research, 51 (5), 891-904. https://doi.org/10.1016/j.asr.2012.10.010
Sen, Z., Oztopal, A. & Sahin, A. D. (2004). Solar Irradiation Estimation from Sunshine Duration by Geno-Fuzzy Partial Approach. Energy Sources, 26, 377–386. https://doi.org/10.1080/00908310490266814
Sözen, A., Arcaklioğlu, E. & Özalp, M. (2004). Estimation of Solar Potential in Turkey by Artificial Neural Networks Using Meteorological and Geographical Data. Energy Conversion and Management, 45 (18-19), 3033–3052. https://doi.org/10.1016/j.enconman.2003.12.020
Sözen, A. & Arcaklioğlu, E. (2005). Solar Potential in Turkey. Applied Energy, 80 (1), 35–45. https://doi.org/10.1016/j.apenergy.2004.02.003
Şenkal, O. & Kuleli, T. (2009). Estimation of Solar Radiation Over Turkey Using Artificial Neural Network and Satellite Data. Applied Energy, 86 (7-8), 1222–1228. https://doi.org/10.1016/j.apenergy.2008.06.003
Şenkal, O. (2010). Modeling of Solar Radiation Using Remote Sensing and Artificial Neural Network in Turkey. Energy, 35 (12), 4795–4801. https://doi.org/10.1016/j.energy.2010.09.009
Taylor, K. E. (2001). Summarizing Multiple Aspects of Model Performance in a Single Diagram, Journal of Geophysical Research Atmospheres, 106 (D7), 7183-7192. https://doi.org/10.1029/2000JD900719
Yildiz, B. Y., Şahin, M., Şenkal, O., Pestemalci, V. & Emrahoğlu, N. (2013). A Comparison of two Solar Radiation Models Using Artificial Neural Networks and Remote Sensing in Turkey. Energy Sources, Part A, 35 (3), 209–217. https://doi.org/10.1080/15567036.2011.650276
Yu, J., Ding, F., Guo, C. & Wang, Y. (2019). System Load Trend Prediction Method Based on IF-EMD-LSTM. International Journal of Distributed Sensor Networks, 15 (8). https://doi.org/10.1177/1550147719867655
Zhang, J., Zhao, L., Deng, S., Xu, W. & Zhang, Y. (2017). A Critical Review of the Models Used to Estimate Solar Radiation. Renewable and Sustainable Energy Reviews, 70, 314–329. http://dx.doi.org/10.1016/j.rser.2016.11.124

Ayrıntılar

Birincil Dil

Türkçe

Konular

Mühendislik

Bölüm

Araştırma Makalesi

Yazarlar

Cahit Gürlek ^*
0000-0002-0273-2999
Türkiye

Mehmet Bilgili
0000-0002-5339-6120
Türkiye

Erken Görünüm Tarihi

16 Haziran 2023

Yayımlanma Tarihi

30 Haziran 2023

Gönderilme Tarihi

25 Mart 2023

Kabul Tarihi

25 Nisan 2023

Yayımlandığı Sayı

Yıl 1970 Cilt: 2 Sayı: 1

IZ

https://izlik.org/JA78YS65RB

APA

Gürlek, C., & Bilgili, M. (2023). Aylık Toplam Güneş Işınımının Uzun-Kısa Süreli Bellek (LSTM) Yöntemiyle Tahmini: Sivas İli Örneği. Sivas Cumhuriyet Üniversitesi Bilim ve Teknoloji Dergisi, 2(1), 24-30. https://izlik.org/JA78YS65RB

Aylık Toplam Güneş Işınımının Uzun-Kısa Süreli Bellek (LSTM) Yöntemiyle Tahmini: Sivas İli Örneği

Estimation of Monthly Global Solar Radiation Using Long-Short Term Memory (LSTM) Method: A Case Study of Sivas Province

Abstract

Keywords