498
Views
0
CrossRef citations to date
0
Altmetric
Research Article

A new approach to short-term wind speed prediction: the prophet model

ORCID Icon, ORCID Icon & ORCID Icon
Pages 8826-8841 | Received 27 Jun 2022, Accepted 02 Sep 2022, Published online: 21 Sep 2022

References

  • Aasim Singh, S. N., A. Mohapatra, and A. Mohapatra. 2019. Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting. Renewable Energy 136:758–68. doi:10.1016/J.RENENE.2019.01.031.
  • Aditya Satrio, C. B., W. Darmawan, B. U. Nadia, and N. Hanafiah. 2021. Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET. Procedia Computer Science 179:524–32. doi:10.1016/J.PROCS.2021.01.036.
  • Arslan, S. 2022. A hybrid forecasting model using LSTM and Prophet for energy consumption with decomposition of time series data. PeerJ Computer Science 8:e1001. doi:10.7717/peerj-cs.1001.
  • Bokde, N. D., Z. M. Yaseen, and G. B. Andersen. 2020. ForecastTb—an R package as a test-bench for time series forecasting—application of wind speed and solar radiation modeling. Energies 13 (10):2578. doi:10.3390/en13102578.
  • Chae, S., S. Kwon, and D. Lee. 2018. Predicting infectious disease using deep learning and big data. International Journal of Environmental Research and Public Health 15 (8). doi:10.3390/IJERPH15081596.
  • Colak, I., S. Sagiroglu, M. Yesilbudak, E. Kabalci, and H. Ibrahim Bulbul (2015). Multi-Time series and-time scale modeling for wind speed and wind power forecasting part II: Medium-term and long-term applications. 2015 International Conference on Renewable Energy Research and Applications, ICRERA 2015, 215–20. 10.1109/ICRERA.2015.7418698
  • Grigonytė, E., and E. Butkevičiūtė. 2016. Short-Term wind speed forecasting using ARIMA model. Energetika 62 (1–2):45–55. doi:10.6001/ENERGETIKA.V62I1-2.3313.
  • Hussin, N. H., F. Yusof, A. R. Jamaludin, and S. M. Norrulashikin. 2021. Forecasting wind speed in peninsular Malaysia: An application of arima and arima-garch models. Pertanika Journal of Science and Technology 29 (1):31–58. doi:10.47836/PJST.29.1.02.
  • Jiang, P., R. Li, and H. Li. 2019. Multi-Objective algorithm for the design of prediction intervals for wind power forecasting model. Applied Mathematical Modelling 67:101–22. doi:10.1016/j.apm.2018.10.019.
  • La Haute Borne Data. (2017). Retrieved from https://opendata-renewables.engie.com/explore/dataset/01c55756-5cd6-4f60-9f63-2d771bb25a1a/information.
  • Liu, H., C. Chen, H. Q. Tian, and Y. F. Li. 2012. A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks. Renewable Energy 48:545–56. doi:10.1016/J.RENENE.2012.06.012.
  • Liu, H., H. Q. Tian, and Y. F. Li. 2015. An EMD-recursive ARIMA method to predict wind speed for railway strong wind warning system. Journal of Wind Engineering and Industrial Aerodynamics 141:27–38. doi:10.1016/J.JWEIA.2015.02.004.
  • Li, H., J. Wang, H. Lu, and Z. Guo. 2018. Research and application of a combined model based on variable weight for short term wind speed forecasting. Renewable Energy 116:669–84. doi:10.1016/J.RENENE.2017.09.089.
  • Nair, K. R., V. Vanitha, and M. Jisma (2017). Forecasting of wind speed using ANN, ARIMA and Hybrid models. In 2017 international conference on intelligent computing, instrumentation and control technologies (ICICICT), Kannur, Kerala India. (pp. 170–75).
  • Radziukynas, V., and A. Klementavicius (2014). Short-Term wind speed forecasting with ARIMA model. 2014 55th International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2014, 145–49. 10.1109/RTUCON.2014.6998223
  • Radziukynas, V., and A. Klementavičius. 2016. Short-Term forecasting of loads and wind power for Latvian power system: Accuracy and capacity of the developed tools. Latvian Journal of Physics and Technical Sciences 53 (2):3–13. doi:10.1515/LPTS-2016-0008.
  • Samal, K. K. R., K. S. Babu, S. K. Das, and A. Acharaya. 2019. Time series based air pollution forecasting using SARIMA and prophet model. ACM International Conference Proceeding Series 80–85. doi:10.1145/3355402.3355417.
  • Santhosh, M., C. Venkaiah, and D. V. Kumar. 2018. Ensemble empirical mode decomposition based adaptive wavelet neural network method for wind speed prediction. Energy Conversion and Management 168:482–93. doi:10.1016/j.enconman.2018.04.099.
  • Schaffer, A. L., T. A. Dobbins, and S. A. Pearson. 2021. Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: A guide for evaluating large-scale health interventions. BMC Medical Research Methodology 21 (1):1–12. doi:10.1186/S12874-021-01235-8/FIGURES/5.
  • Shen, J., D. Valagolam, and S. McCalla. 2020. Prophet forecasting model: A machine learning approach to predict the concentration of air pollutants (PM2. 5, PM10, O3, NO2, SO2, CO) in Seoul, South Korea. PeerJ 8:e9961. doi:10.7717/peerj.9961.
  • Shivani, K. S., and A. R. Nair (2019). A comparative study of ARIMA and RNN for short term wind speed forecasting. In 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kanpur, India (pp. 1–7).
  • Shukur, O. B., and M. H. Lee. 2015. Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA. Renewable Energy 76:637–47. doi:10.1016/j.renene.2014.11.084.
  • Taylor, S. J., and B. Letham. 2018. Forecasting at Scale. 72 (1):37–45. doi:10.1080/00031305.2017.1380080.
  • Toharudin, T., R. S. Pontoh, R. E. Caraka, S. Zahroh, Y. Lee, and R. C. Chen. 2021. Employing long short-term memory and Facebook prophet model in air temperature forecasting. 1–24. doi:10.1080/03610918.2020.1854302.
  • Tyass, I., A. Bellat, A. Raihani, K. Mansouri, and T. Khalili (2022). Wind speed prediction based on seasonal ARIMA model. E3S Web of Conferences, 336, 00034. 10.1051/E3SCONF/202233600034
  • Wilks, D. S. 2019. Statistical methods in atmoshperic sciences. Statistical Methods in the Atmospheric Sciences 100:395.
  • WoS. Retrieved Jun 18, 2022, from https://www.webofscience.com/wos/woscc/summary/f46b26ed-2081-43d7-8196-68ce598af81d-47452a32/relevance/1
  • Yarrington, C. S. 2021. Review of forecasting univariate time-series data with application to water-energy nexus studies & proposal of parallel hybrid SARIMA-ANN model. United States:West Virginia University.
  • Yatiyana, E., S. Rajakaruna, and A. Ghosh. 2018. Wind speed and direction forecasting for wind power generation using ARIMA model. 2017 Australasian Universities Power Engineering Conference, AUPEC 2017 2017 (Novem):1–6. doi:10.1109/AUPEC.2017.8282494.
  • Ye, Z. (2019). Air pollutants prediction in shenzhen based on arima and prophet method. In E3S Web of Conferences, Hefei, China. Vol. 136, p. 05001. EDP Sciences.
  • Yenidogan, I., A. Cayir, O. Kozan, T. Dag, and C. Arslan (2018). Bitcoin forecasting using ARIMA and PROPHET. UBMK 2018 - 3rd International Conference on Computer Science and Engineering, 621–24. 10.1109/UBMK.2018.8566476
  • Yuan, D., Z. Qian, B. Jing, and Y. Pei (2018, November). Short-Term wind speed forecasting using STLSSVM hybrid model. In 2018 International Conference on Power System Technology (POWERCON), Guangzhou, China. (pp. 1661–67). IEEE.
  • Yunus, K., T. Thiringer, and P. Chen. 2015. ARIMA-Based frequency-decomposed modeling of wind speed time series. IEEE Transactions on Power Systems 31 (4):2546–56. doi:10.1109/TPWRS.2015.2468586.
  • Zhao, J., and C. Zhang. 2020, August. Research on sales forecast based on prophet-SARIMA combination model. Journal of Physics Conference Series 1616(1): IOP Publishing 012069.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.