10.1007/s40095-022-00475-8

Wind energy forecasting by fitting predicted probability density functions of wind speed measurements

  • Amir J. Abdul Majid*1,
  1. College of Engineering and Technology, University of Science and Technology of Fujairah, Al Fujairah, AE

Published in Issue 2022-02-18

How to Cite

Abdul Majid, A. J. (2022). Wind energy forecasting by fitting predicted probability density functions of wind speed measurements. International Journal of Energy and Environmental Engineering, 13(2 (June 2022). https://doi.org/10.1007/s40095-022-00475-8
Crossref
Scopus
Google Scholar
Europe PMC

Abstract

Abstract The aim of this work is to forecast wind energy by fitting the wind speed logged data, that have been measured over a year period (Nov. 2019–Mar. 2021), on a unique probability density function selected among a number of similar probability functions, as it is not always possible to select one distribution function that fits all wind speed regimes. The wind speed and direction data were measured at Fujairah site, which are affected by long-term fluctuation of ± 10% of wind speed, and short-term fluctuation of more than ± 20%. Based on the foregoing measurements, five different probability density functions can be fitted, namely Weibull, Rayleigh, Gamma, Lognormal and Exponential, with their associated parameters. A procedural algorithm is proposed for wind speed forecasting with best selected fitting distribution function, using a procedural forecast-check method, in which forecasting is performed with time on the most suitable distribution function that fits the foregoing data, depending on minimum errors accumulated from preceded measurements. Different error estimation methods are applied. The algorithm of selecting different distribution functions with time, makes energy prediction more accurate depending on the fluctuation of wind speed. A detailed probabilistic analysis is carried out to predict probable wind speed, and hence wind energy, based on variations of the parameters of the selected fitting distribution function.

Keywords

  • Energy forecasting,
  • Error estimation,
  • Curve fitting,
  • Probability density functions,
  • Procedural algorithm,
  • Speed prediction

References

  1. Bazionis and Georgilakis (2021) Reviews of deterministic and probabilistic wind power forecasting: models, methods (pp. 13-47) https://doi.org/10.3390/electricity2010002
  2. Wu, Y.K., Po, E.S.; Jing, S.H.: An overview of wind power probabilistic forecasts. In: Proceedings of the IEEE PES Asia-Pacific Power and Energy Engineering Conference, Xi’an, China, 25–28 October 2016
  3. Giebel, G., Brownsword, R., Kariniotakis, G., Denhart, M., Draxl, C.: The state-of-the-art in short-term prediction of wind power: A literature overview, 2nd ed. Available online:
  4. https://orbit.dtu.dk/en/publications/the-state-of-the-art-in-short-termprediction-of-wind-power-a-lit
  5. (accessed on 10 October 2020)
  6. Cadenas and Rivera (2010) Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model (pp. 2732-2738) https://doi.org/10.1016/j.renene.2010.04.022
  7. Catalão et al. (2011) Short-term wind power forecasting in Portugal by neural networks and wavelet transform (pp. 1245-1251) https://doi.org/10.1016/j.renene.2010.09.016
  8. Gomes and Castro (2012) Wind speed and wind power forecasting using statistical models: auto regressive moving average (ARMA) and artificial neural networks (ANN) (pp. 41-50) https://doi.org/10.20533/ijsed.2046.3707.2012.0007
  9. Cao, Y., Liu, Y., Zhang, D., Wang, W., Chen, Z.: Wind power ultra-short-term forecasting method combined with pattern-matching and ARMA-model. In: Proceedings of the IEEE Power Tech, Grenoble, France, 16–20 June 2013
  10. Tseng et al. (2001) Applied hybrid Grey model to forecast seasonal time series (pp. 291-302) https://doi.org/10.1016/S0040-1625(99)00098-0
  11. Catalao, J.P.S., Pousinho, H.M.I., Mendez, V.M.F.: An artificial neural network approach for short-term wind power forecasting in Portugal. In: Proceedings of the 15th International Conference of Intelligent System Applications to Power Systems, Curitiba, Brazil, 8–12 November 2009
  12. Haque et al. (2014) A Hybrid intelligent model for deterministic and quantile regression approach for probabilistic wind power forecasting (pp. 1663-1672) https://doi.org/10.1109/TPWRS.2014.2299801
  13. Bofinger, S., Luig, A., Beyer, H.: Qualification of wind power forecasts. In: Proceedings of the Global Wind Power Conference, Paris, France, 2–5 April 2002
  14. Zeng, J., Qiao,W.: Support vector machine-based short-term wind power forecasting. In: Proceedings of the IEEE/PES Power Systems Conference and Exposition, Phoenix, AZ, USA, 20–23 March 2011
  15. Zhang, W., Liu, F., Zheng, X., Li, Y.: A hybrid EMD-SVM based short-term wind power forecasting model. In: Proceedings of the IEEE PES Asia-Pacific Power and Energy Engineering Conference, Brisbane, QLD, Australia, 15–18 November 2015.
  16. Hui et al. (2020) A new hybrid ensemble deep reinforcement learning model for wind speed short term forecasting https://doi.org/10.1016/j.energy.2020.117794
  17. Wang et al. (2012) A chance-constrained two-stage stochastic program for unit commitment with uncertain wind power output (pp. 206-215) https://doi.org/10.1109/TPWRS.2011.2159522
  18. Wan et al. (2017) Direct quantile regression for nonparametric probabilistic forecasting of wind power generation (pp. 2767-2778) https://doi.org/10.1109/TPWRS.2016.2625101
  19. Juban, J., Siebert, N., Kariniotakis, G.: Probabilistic short-term wind power forecasting for the optimal management of wind generation. In: Proceedings of the IEEE Power Tech, Lausanne, Switzerland, 1–5 July 2007.
  20. Khosravi et al. (2013) Prediction intervals for short-term wind farm power generation forecasts (pp. 602-610) https://doi.org/10.1109/TSTE.2012.2232944
  21. Quan et al. (2014) Short-term load and wind power forecasting using neural network-based prediction intervals (pp. 303-315) https://doi.org/10.1109/TNNLS.2013.2276053
  22. Wan et al. (2014) Probabilistic forecasting of wind power generation using extreme learning machine (pp. 1033-1044) https://doi.org/10.1109/TPWRS.2013.2287871
  23. Wu et al. (2021) Forecast of wind power generation with data processing and numerical weather prediction (pp. 36-45) https://doi.org/10.1109/TIA.2020.3037264
  24. Wu et al. (2018) Probabilistic wind power forecasting using weather ensemble models (pp. 5609-5620) https://doi.org/10.1109/TIA.2018.2858183
  25. Afrasiabi et al. (2021) Advanced deep learning approach for probabilistic wind speed forecasting (pp. 720-727) https://doi.org/10.1109/TII.2020.3004436
  26. Dehnavi, S.D.; Shirani, A.; Mehrjerdi, H.; Baziar, M. New deep learning-based approach for the wind turbine output power modeling and forecasting. IEEE Trans. Ind. Appl. 2020, ISSN:0093-9994, Electronic ISSN: 1939–9367
  27. https://doi.org/10.1109/TIA.2020.3002186
  28. Liu et al. (2020) A novel deep learning approach for wind power forecasting based on WD-LSTM model https://doi.org/10.3390/en13184964
  29. Viet et al. (2020) Models for short-term wind power forecasting based on improved artificial neural network using particle swarm optimization and genetic algorithms https://doi.org/10.3390/en13112873
  30. Kim and Hur (2020) An ensemble forecasting model of wind power outputs based on improved statistical approaches https://doi.org/10.3390/en13051071
  31. Cui et al. (2017) A data-driven methodology for probabilistic wind power ramp forecasting (pp. 1326-1338) https://doi.org/10.1109/TSG.2017.2763827
  32. Zhang et al. (2013) A versatile probability distribution model for wind power forecast errors and its application in economic dispatch (pp. 3114-3125) https://doi.org/10.1109/TPWRS.2013.2249596
  33. Chen et al. (2014) Wind power forecasts using Gaussian processes and numerical weather prediction (pp. 656-665) https://doi.org/10.1109/TPWRS.2013.2282366
  34. Rajagopalan, S.; Santoso, S.: Wind power forecasting and error analysis using the autoregressive moving average modeling. In: Proceedings of the IEEE Power & Energy Society General Meeting, Calgary, AB, Canada, 26–30 July 2009.
  35. Tsikalakis et al. (2009) Impact of wind power forecasting error bias on the economic operation of autonomous power systems (pp. 315-331) https://doi.org/10.1002/we.294
  36. Sun and Zhao (2020) Short-term wind power forecasting based on VMD decomposition, conv LSTM networks and error analysis (pp. 134422-134434) https://doi.org/10.1109/ACCESS.2020.3011060
  37. Miller, S., Childers, D.: Multiple random variables. In: Probability and random processes with Applications to Signal Processing and Communications, a book, AP, ISBN: 978-0-12-386981-4, 2012
  38. Hodge, B.K.: Wind energy. In: Alternative Energy Systems and Applications, a book, pp. 56–87, John Wiley, ISBN: 978-0-470-14250-9, 2010
  39. Majid, A.: The evaluation of wind energy based on the inherent nature of wind speed assessment at Fujairah (UAE). Instrumentation Mesure Métrologie
  40. 20
  41. (3), 121–130 (2021)