Öz
Güneş enerjisinin potansiyelinden tam anlamıyla faydalanmak ve güneş enerjisi sistemlerini etkin bir şekilde işletebilmek için güneş ışınımı değerinin bilinmesi büyük önem arz etmektedir. Yüksek değişkenliğe sahip güneş radyasyonu verilerinin modellenmesi karmaşık bir problemdir ve doğrusal olmayan yöntemlere ihtiyaç vardır. Bu çalışmada, güneş ışınımı tahmini için CNN ve LSTM mimarileri kullanılarak oluşturulan hibrit bir model önerilmiştir. Önerilen modelin performansı ve uygulanabilirliği Rastgele Orman, Karar Ağaçları ve K-En Yakın Komşu gibi farklı makine öğrenmesi yöntemleriyle karşılaştırılarak incelenmiştir. Çalışmada, Afyon Kocatepe Üniversitesi yerleşkesine konumlandırılan bir piranometre ile saatlik olarak ölçülmüş güneş ışınımı değerleri kullanılmıştır. Deney sonuçları, önerilen CNN-LSTM modelinin diğer yöntemlere oranla daha başarılı sonuçlar verdiğini ortaya koymuştur.
Anahtar Kelimeler
Güneş Işınımı Tahmini Derin Öğrenme Uzun-Kısa Süreli Bellek Zaman Serisi Tahmini Makine Öğrenmesi
Kaynakça
- 1. Öztürk H., Dünyada ve Türkiye'de güneş enerjisinden elektrik üretimi: güncel piyasa gelişmeleri ve beklentiler, 2022.
- 2. Chu Y, Pedro H.T, Coimbra C.F., Hybrid intra-hour DNI forecasts with sky image processing enhanced by stochastic learning, Solar Energy, 98, 592-603, 2013.
- 3. Qing X, Niu Y., Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM, Energy, 148, 461-8, 2018.
- 4. Kara A., Global solar irradiance time series prediction using long short-term memory network, Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 4, 7, 2019.
- 5. Arslan G., Bayhan B., Yaman K., Mersin/Türkiye için ölçülen global güneş ışınımının yapay sinir ağları ile tahmin edilmesi ve yaygın ışınım modelleri ile karşılaştırılması, Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 7, 80-96, 2019.
- 6. Ghimire S., Deo R.C., Raj N., Mi J., Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms, Applied Energy, 253, 113541, 2019.
- 7. Kumari P., Toshniwal D., Long short term memory–convolutional neural network based deep hybrid approach for solar irradiance forecasting, Applied Energy, 295, 117061, 2021.
- 8. Jalali S.M.J., Khodayar M., Ahmadian S., Shafie-Khah M., Khosravi A., Islam S.M.S, A new ensemble reinforcement learning strategy for solar irradiance forecasting using deep optimized convolutional neural network models, 2021 International Conference on Smart Energy Systems and Technologies (SEST): IEEE, 1-6, 2021.
- 9. Haider S.A., Sajid M., Sajid H., Uddin E., Ayaz Y., Deep learning and statistical methods for short-and long-term solar irradiance forecasting for Islamabad, Renewable Energy,198, 51-60, 2022.
- 10. Gao B., Huang X., Shi J., Tai Y., Zhang J., Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks, Renewable Energy, 162, 1665-83, 2020.
- 11. Acikgoz H., A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting, Applied Energy, 305, 117912, 2022.
- 12. Sorkun M.C., Incel Ö.D., Paoli C., Time series forecasting on multivariate solar radiation data using deep learning (LSTM), Turkish Journal of Electrical Engineering and Computer Sciences, 28, 211-23, 2020.
- 13. Erturan M.B., Merdivenci F., Optimized ARIMA-ANN hybrid model for time series analysis, Journal of the Faculty of Engineering and Architecture of Gazi University, 37 (2), 1019-1032, 2022.
- 14. LeCun Y., Bottou L., Bengio Y., Haffner P., Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86, 2278-324, 1998.
- 15. Qin L., Yu N., Zhao D., Applying the convolutional neural network deep learning technology to behavioural recognition in intelligent video, Tehnički vjesnik, 25, 528-35, 2018.
- 16. Pascanu R., Mikolov T., Bengio Y., On the difficulty of training recurrent neural networks, International conference on machine learning: PMLR, 1310-8, 2013.
- 17. Begüm E., İnkaya T., Long short-term memory network based deep transfer learning approach for sales forecasting, Journal of the Faculty of Engineering and Architecture of Gazi University, 39 (1), 191-202, 2024.
- 18. Bouktif S., Fiaz A., Ouni A., Serhani M.A., Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches, Energies,11, 1636, 2018.
Öz
To fully utilize the potential of solar energy and effectively operate solar energy systems, it is vital to know solar radiation values. Modeling solar radiation data with high variability is a complex problem, and nonlinear methods are needed. This study proposes a hybrid model using CNN and LSTM architectures for solar radiation prediction. The performance and applicability of the proposed model are examined by comparing it with different machine learning methods such as Random Forest, Decision Tree and K-Nearest Neighbor. The study used hourly solar radiation values measured with a pyranometer on the Afyon Kocatepe University campus. Experiment results revealed that the proposed CNN-LSTM model gave more successful results than other methods.
Anahtar Kelimeler
Solar Radiation Prediction Deep Learning Time Series Prediction Long-Short-Term Memory Machine learning
Kaynakça
- 1. Öztürk H., Dünyada ve Türkiye'de güneş enerjisinden elektrik üretimi: güncel piyasa gelişmeleri ve beklentiler, 2022.
- 2. Chu Y, Pedro H.T, Coimbra C.F., Hybrid intra-hour DNI forecasts with sky image processing enhanced by stochastic learning, Solar Energy, 98, 592-603, 2013.
- 3. Qing X, Niu Y., Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM, Energy, 148, 461-8, 2018.
- 4. Kara A., Global solar irradiance time series prediction using long short-term memory network, Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 4, 7, 2019.
- 5. Arslan G., Bayhan B., Yaman K., Mersin/Türkiye için ölçülen global güneş ışınımının yapay sinir ağları ile tahmin edilmesi ve yaygın ışınım modelleri ile karşılaştırılması, Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 7, 80-96, 2019.
- 6. Ghimire S., Deo R.C., Raj N., Mi J., Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms, Applied Energy, 253, 113541, 2019.
- 7. Kumari P., Toshniwal D., Long short term memory–convolutional neural network based deep hybrid approach for solar irradiance forecasting, Applied Energy, 295, 117061, 2021.
- 8. Jalali S.M.J., Khodayar M., Ahmadian S., Shafie-Khah M., Khosravi A., Islam S.M.S, A new ensemble reinforcement learning strategy for solar irradiance forecasting using deep optimized convolutional neural network models, 2021 International Conference on Smart Energy Systems and Technologies (SEST): IEEE, 1-6, 2021.
- 9. Haider S.A., Sajid M., Sajid H., Uddin E., Ayaz Y., Deep learning and statistical methods for short-and long-term solar irradiance forecasting for Islamabad, Renewable Energy,198, 51-60, 2022.
- 10. Gao B., Huang X., Shi J., Tai Y., Zhang J., Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks, Renewable Energy, 162, 1665-83, 2020.
- 11. Acikgoz H., A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting, Applied Energy, 305, 117912, 2022.
- 12. Sorkun M.C., Incel Ö.D., Paoli C., Time series forecasting on multivariate solar radiation data using deep learning (LSTM), Turkish Journal of Electrical Engineering and Computer Sciences, 28, 211-23, 2020.
- 13. Erturan M.B., Merdivenci F., Optimized ARIMA-ANN hybrid model for time series analysis, Journal of the Faculty of Engineering and Architecture of Gazi University, 37 (2), 1019-1032, 2022.
- 14. LeCun Y., Bottou L., Bengio Y., Haffner P., Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86, 2278-324, 1998.
- 15. Qin L., Yu N., Zhao D., Applying the convolutional neural network deep learning technology to behavioural recognition in intelligent video, Tehnički vjesnik, 25, 528-35, 2018.
- 16. Pascanu R., Mikolov T., Bengio Y., On the difficulty of training recurrent neural networks, International conference on machine learning: PMLR, 1310-8, 2013.
- 17. Begüm E., İnkaya T., Long short-term memory network based deep transfer learning approach for sales forecasting, Journal of the Faculty of Engineering and Architecture of Gazi University, 39 (1), 191-202, 2024.
- 18. Bouktif S., Fiaz A., Ouni A., Serhani M.A., Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches, Energies,11, 1636, 2018.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Erken Görünüm Tarihi | 17 Mayıs 2024 |
| Yayımlanma Tarihi | 20 Mayıs 2024 |
| Gönderilme Tarihi | 28 Ocak 2023 |
| Kabul Tarihi | 26 Kasım 2023 |
| Yayımlandığı Sayı | Yıl 2024 Cilt: 39 Sayı: 4 |