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Çukurova Bölgesi için Kısa Vadeli Yapay Zeka Tabanlı Rüzgar Güç Tahmini

Year 2022, Volume: 37 Issue: 4, 1143 - 1154, 30.12.2022
https://doi.org/10.21605/cukurovaumfd.1230965

Abstract

Son yıllarda hızla artan nüfus ve sanayi artışının yarattığı enerji ihtiyacı kısıtlı kaynaklarla karşılanamaz hale gelmiştir. Enerji üretimi ve tüketimi arasında oluşan farklılıklar, kısıtlı kaynakların yerini yenilenebilir enerjilerin gelişimine bırakmıştır. Sağlığımızı tehdit eden unsurları en aza indirmeyi planlayan Avrupa Yeşil Mütabakatı, 2030 yılına kadar dünya genelinde yenilenemez enerjilerin kullanımını minimum seviyelere indirecektir. Ayrıca iklim krizinin, sera gazı salınımını önemli ölçüde etkileyeceği ve doğaya zarar vereceği öngörülmektedir. Karbon emisyonunun sıfıra indirilebilmesi prensibinde, rüzgar gücü tahmini çalışmaları oldukça önemlidir. Ancak rüzgar enerjisinde yaşanan sıkıntı, üretiminin meterolojik şartlar doğrultusunda sürekli değişmesidir. Voltaj ve frekans değişiklerinin yarattığı enerji kararsızlığının önüne geçilebilmesi için denge şebekelerdeki üretim ve tüketimin sürekli olarak sağlanması gerekmektedir. Sistemlerin modelleme süresini ve doğruluğunu etkileyen rüzgar hızındaki doğrusal olmayan bu değişiklikler, enerjisi kayıplarının en aza indirilebilmesi için önemlidir. Bu çalışmada, Çukurova Bölgesi’nden elde edilen gerçek kısa vadeli rüzgar gücü verileri araştırma nesnesi olarak alınmış, MPE-MAPE tasarlanan tahmin modellerinin performans indekslerini karşılaştırmak için kullanılmıştır.

References

  • ⦁ Kumar, S., Sahay, K.B., 2018. Wind Speed Forecasting Using Different Neural Network Algorithms. 2018 2nd International Conference on Electronics. Materials Engineering & Nano- Technology (IEMENTech), 1–4.
  • ⦁ Ibrahim, M., Alsheikh, A., Al, Q., Al-Dahidi, S., Elmoaqet, H., 2020. Short-Time Wind Speed Forecast Using Artificial Learning- Based Algorithms. Computational Intelligent Neuroscience, 2020, 1–15.
  • ⦁ Liu, Z., Jiang, P., Zhang, L., Niu, X., 2020. A Combined Forecasting Model for Time Series: Application to Short-Term Wind Speed Forecasting. Applied Enegy, 259, Article 114137.
  • ⦁ Zhang, Y., Pan, G., Chen, B., Han, J., Zhao, Y., Zhang, C., 2020. Short-Term Wind Speed Prediction Model Based on GA-ANN Improved by VMD. Renewable Energy, 156, 1373-1388.
  • ⦁ Hur, S., 2021. Short-Term Wind Speed Prediction Using Extended Kalman Filter and Machine Learning. Energy Reports, 7, 1046-1054.
  • ⦁ Jiang, P., Liu, Z., Niu, X., Zhang, L., 2021. A Combined Forecasting System Based on Statistical Method, Artificial Neural Networks, and Deep Learning Methods for Short-Term Wind Speed Forecasting, Energy, 217, 119361. Li, W., Jia, X., Li, X., Wang, Y., Lee, J., 2021. A Markov Model for Short Term Wind Speed Prediction by Integrating the Wind Acceleration Information, Renewable Energy, 164, 242-253.
  • ⦁ Zhang, Y., Li, R., Zhang, J., 2021. Optimization Scheme of Wind Energy Prediction Based on Artificial Intelligence, Environmental Science and Pollution Research, 28, 39966-39981.
  • ⦁ Kosanoğlu, F., Kiriş, Z., Beyca Ö., 2022. Tekrarlayan Sinir Ağları Temelli Rüzgar Hızı Modelleri: Yalova Bölgesinde Bir Uygulama. Zeki Sistemler Teori ve Uygulama Dergisi, 5(2), 178-188.
  • ⦁ Tang, C., Tao, X., Wei, Y., Tong, Z., Zhu, F., Lin, H., 2022. Analysis and Prediction of Wind Speed Effects in East Asia and the Western Pacific Based on Multi-Source Data. Sustainability, 14, 12089.
  • ⦁ Wickramasinghe, L., Ekanayake, P., Jayasinghe, J., 2022. Machine Learning and Statistical Techiques for Daily Wind Energy Prediction. Gazi University Journal of Science, 35(4), 1359-1370.
  • ⦁ Yang, Y., Javanroodi, K., Nik, V., 2022. Climate Change and Renewable Energy Generarion in Europe-Long-Term Impact Assessment on Solar and Wind Energy Using High-Resolution Future Climate Data and Considering Climate Uncertainties. Energies, 15(1), 302.
  • ⦁ Zhang, Y., Chen, Y., 2022. Application of Hybrid Model Based on CEEMDAN, SVD, PSO to Wind Energy Prediction. Enviromental Science and Pollution Research, 29, 22661-22674.
  • ⦁ Ceylan Z., Bulkan S., 2018. Türkiye Ulaşım Kaynaklı Enerji İhtiyacının Hibrit ANFIS-PSO Metodu ile Tahmini. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 18, 740-750.

Short Term Artificial Intelligence Based Wind Power Forecastting for Çukurova Region

Year 2022, Volume: 37 Issue: 4, 1143 - 1154, 30.12.2022
https://doi.org/10.21605/cukurovaumfd.1230965

Abstract

In recent years, the energy need created by the rapidly increasing population and industrial growth has become unable to be met with limited resources. The differences between energy production and consumption have left the place of limited resources to the development of renewable energy sources. The European Green Deal, which plans to minimize the threats to our health, will reduce the use of non- renewable energies around the world to minimum levels by 2030. In addition, it is predicted that the climate crisis will significantly affect greenhouse gas emissions and harm nature. Because of these, wind speed estimation studies are very important in the principle of reducing carbon emissions to zero. However, the problem experienced in wind energy is that its production is constantly changing in line with meteorological conditions. In order to prevent the energy instability caused by voltage and frequency changes, the production and consumption in the balance networks must be ensured continuously. These nonlinear changes in wind speed, which affect the modeling time and accuracy of the systems, are important for minimizing energy losses. Within the scope of the study, real short-term wind power data obtained from Çukurova Region is taken as research object and MPE-MAPE are used to compare the performance indexes of the designed forecast models.

References

  • ⦁ Kumar, S., Sahay, K.B., 2018. Wind Speed Forecasting Using Different Neural Network Algorithms. 2018 2nd International Conference on Electronics. Materials Engineering & Nano- Technology (IEMENTech), 1–4.
  • ⦁ Ibrahim, M., Alsheikh, A., Al, Q., Al-Dahidi, S., Elmoaqet, H., 2020. Short-Time Wind Speed Forecast Using Artificial Learning- Based Algorithms. Computational Intelligent Neuroscience, 2020, 1–15.
  • ⦁ Liu, Z., Jiang, P., Zhang, L., Niu, X., 2020. A Combined Forecasting Model for Time Series: Application to Short-Term Wind Speed Forecasting. Applied Enegy, 259, Article 114137.
  • ⦁ Zhang, Y., Pan, G., Chen, B., Han, J., Zhao, Y., Zhang, C., 2020. Short-Term Wind Speed Prediction Model Based on GA-ANN Improved by VMD. Renewable Energy, 156, 1373-1388.
  • ⦁ Hur, S., 2021. Short-Term Wind Speed Prediction Using Extended Kalman Filter and Machine Learning. Energy Reports, 7, 1046-1054.
  • ⦁ Jiang, P., Liu, Z., Niu, X., Zhang, L., 2021. A Combined Forecasting System Based on Statistical Method, Artificial Neural Networks, and Deep Learning Methods for Short-Term Wind Speed Forecasting, Energy, 217, 119361. Li, W., Jia, X., Li, X., Wang, Y., Lee, J., 2021. A Markov Model for Short Term Wind Speed Prediction by Integrating the Wind Acceleration Information, Renewable Energy, 164, 242-253.
  • ⦁ Zhang, Y., Li, R., Zhang, J., 2021. Optimization Scheme of Wind Energy Prediction Based on Artificial Intelligence, Environmental Science and Pollution Research, 28, 39966-39981.
  • ⦁ Kosanoğlu, F., Kiriş, Z., Beyca Ö., 2022. Tekrarlayan Sinir Ağları Temelli Rüzgar Hızı Modelleri: Yalova Bölgesinde Bir Uygulama. Zeki Sistemler Teori ve Uygulama Dergisi, 5(2), 178-188.
  • ⦁ Tang, C., Tao, X., Wei, Y., Tong, Z., Zhu, F., Lin, H., 2022. Analysis and Prediction of Wind Speed Effects in East Asia and the Western Pacific Based on Multi-Source Data. Sustainability, 14, 12089.
  • ⦁ Wickramasinghe, L., Ekanayake, P., Jayasinghe, J., 2022. Machine Learning and Statistical Techiques for Daily Wind Energy Prediction. Gazi University Journal of Science, 35(4), 1359-1370.
  • ⦁ Yang, Y., Javanroodi, K., Nik, V., 2022. Climate Change and Renewable Energy Generarion in Europe-Long-Term Impact Assessment on Solar and Wind Energy Using High-Resolution Future Climate Data and Considering Climate Uncertainties. Energies, 15(1), 302.
  • ⦁ Zhang, Y., Chen, Y., 2022. Application of Hybrid Model Based on CEEMDAN, SVD, PSO to Wind Energy Prediction. Enviromental Science and Pollution Research, 29, 22661-22674.
  • ⦁ Ceylan Z., Bulkan S., 2018. Türkiye Ulaşım Kaynaklı Enerji İhtiyacının Hibrit ANFIS-PSO Metodu ile Tahmini. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 18, 740-750.
There are 13 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Pırıl Tekin This is me 0000-0002-2326-7571

Publication Date December 30, 2022
Published in Issue Year 2022 Volume: 37 Issue: 4

Cite

APA Tekin, P. (2022). Çukurova Bölgesi için Kısa Vadeli Yapay Zeka Tabanlı Rüzgar Güç Tahmini. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 37(4), 1143-1154. https://doi.org/10.21605/cukurovaumfd.1230965