Autoregressive integrated moving average (ARIMA) modeling for wind resource assessment
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s42108-023-00254-z.pdf
Reference36 articles.
1. Aasim, S. S. N., & Mohapatra, A. (2019). Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting. Renewable Energy, 136, 758–768. https://doi.org/10.1016/j.renene.2019.01.031
2. Aized, T., Shahid, M., Bhatti, A. A., Saleem, M., & Anandarajah, G. (2017). Energy security and renewable energy policy analysis of Pakistan. RenEwable and Sustainable Energy Reviews, 2018(84), 155–169. https://doi.org/10.1016/j.rser.2017.05.254
3. Baloch, M. H., Kaloi, G. S., & Memon, Z. A. (2016). Current scenario of the wind energy in Pakistan challenges and future perspectives: a case study. Energy Reprts., 2, 201–210. https://doi.org/10.1016/j.egyr.2016.08.002
4. Brockwell, P.J., Davis, R.A. (2023) Introduction to Time Series and Forecasting, Second Edition Springer Texts in Statistics.
5. Chehouri, A., Younes, R., Ilinca, A., & Perron, J. (2015). Review of performance optimization techniques applied to wind turbines. Applied Energy, 142, 361–388. https://doi.org/10.1016/j.apenergy.2014.12.043
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