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Improving Deep Learning Forecasting Model Based on LSTM for Türkiye’s Hydro-Electricity Generation

Yıl 2024, Cilt: 7 Sayı: 3, 325 - 337

Mehmet Bulut

https://doi.org/10.35377/saucis...1503018

Öz

Electricity production in hydraulic power plants depends on the amount of water coming into the basin. This varies depending on precipitation such as snow and rain during the year, but when looking at the years, production is shaped according to the years when meteorological data are similar to each other. LSTM (Long Short-Term Memory) plays an important role in hydropower forecasting, as it is a special artificial neural network designed to model complex relationships on time series data, which is affected by various meteorological factors such as precipitation, temperature, and hydrological data such as water level, such as hydroelectric power production. Therefore, in this study, a forecast system based on the LSTM network model which is one of the deep learning methods was proposed for monthly hydropower-based electricity production forecast in Türkiye. The developed deep learning-based hydropower forecast model provides future production planning based on time series based on actual hydropower production data. Using real production data and LSTM learning models of different structures, monthly hydraulic electricity production forecasts for the next year were made and the models' performances were examined. As a result of this study, RMSE 32.4245 and MAPE 16.03% values and 200-layer LSTM model trained with 12-year data with 144 monthly data points containing hydroelectric generation information was obtained as the best model, and the performance values of the model showed that it was the correct forecasting model. The overall efficiency parameters of the found LSTM model were checked with NSE 0.5398 and KGE 0.8413 values. The performance of the model was found to be a high-accuracy model within acceptable limits and with a correlation value of R2 0.9035 to be very close to reality. The results obtained from this study have shown that deep learning models developed based on many years of production data give successful results in hydroelectric production prediction and can be used as a basis for electricity production planning.

Anahtar Kelimeler

Hydroelectric power Electricity production forecasting deep learning Long short term memory

Etik Beyan

Ethical approval: The conducted research is not related to either human or animal use. Conflict of interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper

Kaynakça

  • D. Gielen, F. Boshell, D. Saygin, M.D. Bazilian, N. Wagner, R. Gorini, “The role of renewable energy in the global energy transformation”, Energy Strategy Reviews, 24: 38-50, 2019.
  • P.A. Owusu, S. Asumadu-Sarkodie, “A review of renewable energy sources, sustainability issues and climate change mitigation”, Cogent Engineering, 3(1):1167990, 2016.
  • M.O. Abdeen, “Energy use and environmental impacts: A general review”, Journal of Renewable and Sustainable Energy, 1:053101, 2009.
  • H. Alrayess, Gharbia S., Beden N., A. Ulke, “Using Machine Learning Techniques and Deep Learning in Forecasting the Hydroelectric Power Generation in Almus Dam, Türkiye”, 5th International Symposium on Dam Safety, Istanbul,Türkiye, Oct. 27-31, 2018.
  • F. Kratzert, Klotz D., Brenner C., Schulz K., M. Herrnegger, “Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks”, Hydrology and Earth System Sciences, 22: 6005–6022, 2018.
  • K. Amarasinghe, Marino, D.L., Manic, M. “Deep neural networks for energy load forecasting”, IEEE 26th Int. Symposium on Industrial Electronics (ISIE), Edinburgh, 1483-1488, 2017.
  • Choi, J.Y., Lee, B, ”Combining LSTM Network Ensemble via Adaptive Weighting for Improved Time Series Forecasting”, Hindawi Mathematical Problems in Engineering, 2470171:1-8, 2018.
  • Wang, H., Lei, Z., Zhang, X., Zhou, B. and Peng, J. (2019). A review of deep learning for renewable energy forecasting”, Energy Conversion and Management, 198:111799.
  • Y. Cheng, Xu C., Mashima, D., Thing, V., and Y. Wu, “PowerLSTM: Power Demand Forecasting Using Long Short-Term Memory Neural Network”, In: Cong G., Peng WC., Zhang W., Li C., Sun A. (eds) Advanced Data Mining and Applications. ADMA, 727-740, 2017.
  • N. Al Khafaf, Jalili M., P. Sokolowski, “Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting”, in: Macintyre J., Iliadis L., Maglogiannis I., Jayne C. (eds) Engineering Applications of Neural Networks. EANN, 31-42, 2019.
  • J. Zheng, Cencen, Xu, Zhang, Z., and L. Xiaohua, “Electric load forecasting in smart grids using Long-Short-Term-Memory based Recurrent Neural Network”, 51st Annual Conference on Information Sciences and Systems (CISS), Baltimore, MD, 1-6, 2017.
  • E. Choi, Cho, S., D.K. Kim, “Power Demand Forecasting Using Long Short-Term Memory (LSTM) Deep-Learning Model for Monitoring Energy Sustainability”, Sustainability, 12:1109, 2020.
  • D. Altunkaya, Yilmaz, B., “Multivariate Short-term Load Forecasting Using Deep Learning Algorithms”, the Eurasia Proceedings of Science, Technology, Engineering & Mathematics (EPSTEM), 11:14-19, 2020.
  • T.G. Barbounis, Theocharis J.B., Alexiadis M.C., and Dokopoulos P.S., “Long-term wind speed and power forecasting using local recurrent neural network models”, IEEE Transactions on Energy Conversion, 21(1):273-284, 2006.
  • Li, L., Yao, F., Huang, Y., and Zhou, F., "Hydropower Generation Forecasting via Deep Neural Network", 2019 6th International Conference on Information Science and Control Engineering (ICISCE), Shanghai, China, 324-328, (2019).
  • R.Torres, Dorado, F., and Durán, J., “Energy Demand Forecasting Using Deep Learning: Applications for the French Grid”, Energies, 13(9):2242, 2020.
  • O. Yuksek, Komurcu, M.I., Yuksel, I., and K. Kaygusuz, “The role of hydropower in meeting Türkiye’s electric energy demand”, Energy Policy, 34:3093–3103, 2006.
  • I. Kollia and Kollias, S., “A Deep Learning Approach for Load Demand Forecasting of Power Systems”, IEEE Symposium Series on Computational Intelligence (SSCI), India, 912-919, 2018.
  • He, W., “Load Forecasting via Deep Neural Networks”, Procedia Computer Science, 122:308-314, 2017.
  • M. Bulut, Tora H. and B. Magdi, "Comparison of Three Different Learning Methods of Multilayer Perceptron Neural Network for Wind Speed Forecasting", Gazi University Journal of Science, 34.2: 439-454, 2021.
  • R. Ünlü,“A Comparative Study of Machine Learning and Deep Learning for Time Series Forecasting: A Case Study of Choosing the Best Prediction Model for Türkiye Electricity Production”, Suleyman Demirel University Journal of Natural and Applied Sciences, 23:635-646, 2019.
  • S. Bernstein, J. Spring and D. Stanway, Insight: Droughts shrink hydropower, pose risk to global push to clean energy, August 14, 2023, https://www.reuters.com/business/sustainable-business/inconvenient-truth-droughts-shrink-hydropower-pose-risk-global-push-clean-energy-2021-08-13/ [Accessed : 05.08.2024].
  • F. A. Kaplan, The Effect of Climate Change On Hydroelectric Power Plants and Hepp, Ges Hybrid Model, Dsi Technical Bulletin Issue: 141, January 2023
  • M. Bulut, İzzet A. "Prospects of renewable energy penetration to Türkiye's electric power grid", International Journal of Global Warming 25:3-4, 366-377, 2021.
  • Türkiye Electricity Transmission Inc., (TEİAŞ) https://www.teias.gov.tr/turkiye-elektrik-uretim-iletim-istatistikleri. [Accessed : 22.09.2020].
  • Electricity Market Regulatory Authority (EMRA) of Türkiye, https://www.epdk.gov.tr/Detay/Icerik/3-0-167/resmi-istatistikleri. [Accessed : 23.09.2020].
  • Turkish Load Dispatch Information System (YTBS), https://ytbsbilgi.teias.gov.tr/ytbsbilgi/frm_istatistikler.jsf. [Accessed : 22.09.2020].
  • G. Montavon, Samek, W., K-R. Müller. “Methods for interpreting and understanding deep neural networks”, Digital Signal Processing, 73:1–15, 2018
  • A. Sherstinsky, “Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network, Physica D: Nonlinear Phenomena, 404:132306, 2020.
  • S. Hochreiter., J. Schmidhuber,“Long short-term memory”, Neural Computing, 9(8):1735–1780, 1997.
  • X. Wang, Y. Zhao and F. Pourpanah, “Recent advances in deep learning”, Int. Journal of Machine Learning and Cybernetics, 11:747–750, 2020.
  • V. Houdt, V., Greg & Mosquera, G. Carlos & Nápoles, ”A Review on the Long Short-Term Memory Model”, Artificial Intelligence Review, 53. 10.1007/s10462-020-09838-1, 2020.

Yıl 2024, Cilt: 7 Sayı: 3, 325 - 337

Mehmet Bulut

https://doi.org/10.35377/saucis...1503018

Öz

Kaynakça

  • D. Gielen, F. Boshell, D. Saygin, M.D. Bazilian, N. Wagner, R. Gorini, “The role of renewable energy in the global energy transformation”, Energy Strategy Reviews, 24: 38-50, 2019.
  • P.A. Owusu, S. Asumadu-Sarkodie, “A review of renewable energy sources, sustainability issues and climate change mitigation”, Cogent Engineering, 3(1):1167990, 2016.
  • M.O. Abdeen, “Energy use and environmental impacts: A general review”, Journal of Renewable and Sustainable Energy, 1:053101, 2009.
  • H. Alrayess, Gharbia S., Beden N., A. Ulke, “Using Machine Learning Techniques and Deep Learning in Forecasting the Hydroelectric Power Generation in Almus Dam, Türkiye”, 5th International Symposium on Dam Safety, Istanbul,Türkiye, Oct. 27-31, 2018.
  • F. Kratzert, Klotz D., Brenner C., Schulz K., M. Herrnegger, “Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks”, Hydrology and Earth System Sciences, 22: 6005–6022, 2018.
  • K. Amarasinghe, Marino, D.L., Manic, M. “Deep neural networks for energy load forecasting”, IEEE 26th Int. Symposium on Industrial Electronics (ISIE), Edinburgh, 1483-1488, 2017.
  • Choi, J.Y., Lee, B, ”Combining LSTM Network Ensemble via Adaptive Weighting for Improved Time Series Forecasting”, Hindawi Mathematical Problems in Engineering, 2470171:1-8, 2018.
  • Wang, H., Lei, Z., Zhang, X., Zhou, B. and Peng, J. (2019). A review of deep learning for renewable energy forecasting”, Energy Conversion and Management, 198:111799.
  • Y. Cheng, Xu C., Mashima, D., Thing, V., and Y. Wu, “PowerLSTM: Power Demand Forecasting Using Long Short-Term Memory Neural Network”, In: Cong G., Peng WC., Zhang W., Li C., Sun A. (eds) Advanced Data Mining and Applications. ADMA, 727-740, 2017.
  • N. Al Khafaf, Jalili M., P. Sokolowski, “Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting”, in: Macintyre J., Iliadis L., Maglogiannis I., Jayne C. (eds) Engineering Applications of Neural Networks. EANN, 31-42, 2019.
  • J. Zheng, Cencen, Xu, Zhang, Z., and L. Xiaohua, “Electric load forecasting in smart grids using Long-Short-Term-Memory based Recurrent Neural Network”, 51st Annual Conference on Information Sciences and Systems (CISS), Baltimore, MD, 1-6, 2017.
  • E. Choi, Cho, S., D.K. Kim, “Power Demand Forecasting Using Long Short-Term Memory (LSTM) Deep-Learning Model for Monitoring Energy Sustainability”, Sustainability, 12:1109, 2020.
  • D. Altunkaya, Yilmaz, B., “Multivariate Short-term Load Forecasting Using Deep Learning Algorithms”, the Eurasia Proceedings of Science, Technology, Engineering & Mathematics (EPSTEM), 11:14-19, 2020.
  • T.G. Barbounis, Theocharis J.B., Alexiadis M.C., and Dokopoulos P.S., “Long-term wind speed and power forecasting using local recurrent neural network models”, IEEE Transactions on Energy Conversion, 21(1):273-284, 2006.
  • Li, L., Yao, F., Huang, Y., and Zhou, F., "Hydropower Generation Forecasting via Deep Neural Network", 2019 6th International Conference on Information Science and Control Engineering (ICISCE), Shanghai, China, 324-328, (2019).
  • R.Torres, Dorado, F., and Durán, J., “Energy Demand Forecasting Using Deep Learning: Applications for the French Grid”, Energies, 13(9):2242, 2020.
  • O. Yuksek, Komurcu, M.I., Yuksel, I., and K. Kaygusuz, “The role of hydropower in meeting Türkiye’s electric energy demand”, Energy Policy, 34:3093–3103, 2006.
  • I. Kollia and Kollias, S., “A Deep Learning Approach for Load Demand Forecasting of Power Systems”, IEEE Symposium Series on Computational Intelligence (SSCI), India, 912-919, 2018.
  • He, W., “Load Forecasting via Deep Neural Networks”, Procedia Computer Science, 122:308-314, 2017.
  • M. Bulut, Tora H. and B. Magdi, "Comparison of Three Different Learning Methods of Multilayer Perceptron Neural Network for Wind Speed Forecasting", Gazi University Journal of Science, 34.2: 439-454, 2021.
  • R. Ünlü,“A Comparative Study of Machine Learning and Deep Learning for Time Series Forecasting: A Case Study of Choosing the Best Prediction Model for Türkiye Electricity Production”, Suleyman Demirel University Journal of Natural and Applied Sciences, 23:635-646, 2019.
  • S. Bernstein, J. Spring and D. Stanway, Insight: Droughts shrink hydropower, pose risk to global push to clean energy, August 14, 2023, https://www.reuters.com/business/sustainable-business/inconvenient-truth-droughts-shrink-hydropower-pose-risk-global-push-clean-energy-2021-08-13/ [Accessed : 05.08.2024].
  • F. A. Kaplan, The Effect of Climate Change On Hydroelectric Power Plants and Hepp, Ges Hybrid Model, Dsi Technical Bulletin Issue: 141, January 2023
  • M. Bulut, İzzet A. "Prospects of renewable energy penetration to Türkiye's electric power grid", International Journal of Global Warming 25:3-4, 366-377, 2021.
  • Türkiye Electricity Transmission Inc., (TEİAŞ) https://www.teias.gov.tr/turkiye-elektrik-uretim-iletim-istatistikleri. [Accessed : 22.09.2020].
  • Electricity Market Regulatory Authority (EMRA) of Türkiye, https://www.epdk.gov.tr/Detay/Icerik/3-0-167/resmi-istatistikleri. [Accessed : 23.09.2020].
  • Turkish Load Dispatch Information System (YTBS), https://ytbsbilgi.teias.gov.tr/ytbsbilgi/frm_istatistikler.jsf. [Accessed : 22.09.2020].
  • G. Montavon, Samek, W., K-R. Müller. “Methods for interpreting and understanding deep neural networks”, Digital Signal Processing, 73:1–15, 2018
  • A. Sherstinsky, “Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network, Physica D: Nonlinear Phenomena, 404:132306, 2020.
  • S. Hochreiter., J. Schmidhuber,“Long short-term memory”, Neural Computing, 9(8):1735–1780, 1997.
  • X. Wang, Y. Zhao and F. Pourpanah, “Recent advances in deep learning”, Int. Journal of Machine Learning and Cybernetics, 11:747–750, 2020.
  • V. Houdt, V., Greg & Mosquera, G. Carlos & Nápoles, ”A Review on the Long Short-Term Memory Model”, Artificial Intelligence Review, 53. 10.1007/s10462-020-09838-1, 2020.
Toplam 32 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Yazılımı
Bölüm Research Article
Yazarlar

Mehmet Bulut 0000-0003-3998-1785

Erken Görünüm Tarihi 30 Ekim 2024
Yayımlanma Tarihi
Gönderilme Tarihi 20 Haziran 2024
Kabul Tarihi 6 Eylül 2024
Yayımlandığı Sayı Yıl 2024Cilt: 7 Sayı: 3

Kaynak Göster

IEEE M. Bulut, “Improving Deep Learning Forecasting Model Based on LSTM for Türkiye’s Hydro-Electricity Generation”, SAUCIS, c. 7, sy. 3, ss. 325–337, 2024, doi: 10.35377/saucis...1503018.

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