Research Interest

Data science

Publications

14. Karaca, B., Ünlü, K.D., Türkan,S. Wavelet-Enhanced Sequence-to-sequence Modeling with Attenation Mechanism for Short-Term Wind Power Forecasting. Cybernatics and Systems, 1-49 (2025). https://doi.org/10.1080/01969722.2025.2521831 

13. Akbal, Y., Ünlü, K.D.  A hybrid deep learning methodology for wind power forecasting based on attention. International Journal of Green Energy, 21(16):3713-3722 (2024). https://doi.org/10.1080/15435075.2024.2399189 

12. Yörük G, Bac U, Yerlikaya-Özkurt F, Ünlü K.D. Strategic Electricity Production Planning of Turkey via Mixed Integer Programming Based on Time Series Forecasting. Mathematics, 11(8):1865 (2023). https://doi.org/10.3390/math11081865 

11. Esager, M.W.M., Ünlü, K.D. Forecasting Air Quality in Tripoli: An Evaluation of Deep Learning Models for Hourly PM2.5 Surface Mass Concentrations. Atmosphere, 14(3):478 (2023). https://doi.org/10.3390/atmos14030478 

10. Eryilmaz, S., Ünlü, K.D. A new generalized δ-shock model and its application to 1-out-of-(m+ 1): G cold standby system. Reliability Engineering & System Safety, 234, 109203 (2023). https://doi.org/10.1016/j.ress.2023.109203   

9. Akdi,Y., Karamanoğlu, Y.E., Ünlü, K.D., Baş, C.  Identifying the cycles in COVID-19 infection: the case of Turkey.  Journal of Applied Statistics, 50:11-12, 2360-2372  (2023). https://doi.org/10.1080/02664763.2022.2028744 

8. Akbal, Y., Ünlü, K.D. A univariate time series methodology based on sequence-to-sequence learning for short to midterm wind power production. Renewable Energy, 200, 832-844 (2022). https://doi.org/10.1016/j.renene.2022.10.055   

7. Ünlü, K.D. A data-driven model to forecast multi-step ahead time series of Turkish daily electricity load. Electronics, 11:10, 1524 (2022). https://doi.org/10.3390/electronics11101524  

6. Akbal, Y., Ünlü, K.D. A deep learning approach to model daily particular matter of Ankara: key features and forecasting. Int. J. Environ. Sci. Technol, 19, 5911–5927 (2022).  https://doi.org/10.1007/s13762-021-03730-3  

5. Başoğlu Kabran, F., Ünlü, K. D. A two-step machine learning approach to predict S&P 500 bubbles. Journal of Applied Statistics,48:13-15, 2231–2238 (2021).  https://doi.org/10.1080/02664763.2020.1823947  

4. Akdi, Y., Gölveren, E., Ünlü, K.D.,Yücel, M.E. Modeling and forecasting of monthly PM2.5 emission of Paris by periodogram-based time series methodology. Environ Monit Assess, 193, 622 (2021). https://doi.org/10.1007/s10661-021-09399-y  

3. Akdi, Y.,Ünlü, K. D.  Periodicity in precipitation and temperature for monthly data of Turkey. Theor Appl Climatol, 143, 957–968 (2021).  https://doi.org/10.1007/s00704-020-03459-y 

2. Okkaoğlu, Y., Akdi, Y., Ünlü, K. D. Daily PM10, periodicity and harmonic regression model: The case of London. Atmospheric Environment, 238, 117755 (2020).   https://doi.org/10.1016/j.atmosenv.2020.117755 

1. Ünlü, K.D., Sezer, A.D. Excessive backlog probabilities of two parallel queues. Annals of Operations Research 293, 141–174 (2020). https://doi.org/10.1007/s10479-019-03324-w