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.
Similar content being viewed by others
References
Bazionis, I.K., Georgilakis, P.S.: Reviews of deterministic and probabilistic wind power forecasting: models, methods. Electricity 2, 13–47 (2021). https://doi.org/10.3390/electricity2010002
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
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: https://orbit.dtu.dk/en/publications/the-state-of-the-art-in-short-termprediction-of-wind-power-a-lit (accessed on 10 October 2020)
Cadenas, E., Rivera, W.: Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model. Renew Energy 35, 2732–2738 (2010)
Catalão, J.P.S., Pousinho, H.M.I., Mendes, V.M.F.: Short-term wind power forecasting in Portugal by neural networks and wavelet transform. Renew. Energy 36, 1245–1251 (2011)
Gomes, P., Castro, R.: Wind speed and wind power forecasting using statistical models: auto regressive moving average (ARMA) and artificial neural networks (ANN). Int. J. Sustain. Energy Dev. 1, 41–50 (2012)
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
Tseng, F., Yu, H., Tzeng, G.: Applied hybrid Grey model to forecast seasonal time series. Technol. Forecast. Soc. Chang. 67, 291–302 (2001)
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
Haque, A., Nehrir, M., Mandal, P.: A Hybrid intelligent model for deterministic and quantile regression approach for probabilistic wind power forecasting. IEEE Trans. Power Syst. 29, 1663–1672 (2014)
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
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
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.
Hui, L., Chengqing, Y., Haiping, W., Zhu, D., Guangxi, Y.: A new hybrid ensemble deep reinforcement learning model for wind speed short term forecasting. Energy 202, 117794 (2020)
Wang, Q., Guan, Y., Wang, J.: A chance-constrained two-stage stochastic program for unit commitment with uncertain wind power output. IEEE Trans. Power Syst. 27, 206–215 (2012)
Wan, C., Lin, J., Wang, J., Song, Y., Dong, Z.Y.: Direct quantile regression for nonparametric probabilistic forecasting of wind power generation. IEEE Trans. Power Syst. 32, 2767–2778 (2017)
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.
Khosravi, A., Nahavandi, S., Creighton, D.: Prediction intervals for short-term wind farm power generation forecasts. IEEE Trans. Sustain. Energy 4, 602–610 (2013)
Quan, H., Srinivasan, D., Khosravi, A.: Short-term load and wind power forecasting using neural network-based prediction intervals. IEEE Trans. Neural Netw. Learn. Syst. 25, 303–315 (2014)
Wan, C., Xu, Z., Pinson, P., Dong, Z.Y., Wong, K.P.: Probabilistic forecasting of wind power generation using extreme learning machine. IEEE Trans. Power Syst. 29, 1033–1044 (2014)
Wu, Y.K., Wu, Y.C., Hong, J.S., Phan, L.H., Quoc, D.P.: Forecast of wind power generation with data processing and numerical weather prediction. IEEE Trans. Ind. Appl. 57, 36–45 (2021)
Wu, Y., Su, P., Wu, T., Hong, J., Hassan, M.: Probabilistic wind power forecasting using weather ensemble models. IEEE Trans. Ind. Appl. 54, 5609–5620 (2018)
Afrasiabi, M., Mohammadi, M., Rastegar, M., Afrasiabi, S.: Advanced deep learning approach for probabilistic wind speed forecasting. IEEE Trans. Ind. Inform. 17, 720–727 (2021)
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 https://doi.org/10.1109/TIA.2020.3002186
Liu, B., Zhao, S., Yu, X., Zhang, L., Wang, Q.: A novel deep learning approach for wind power forecasting based on WD-LSTM model. Energies 13, 4964 (2020)
Viet, D.T., Phuong, V.V., Duong, M.Q., Tran, Q.T.: Models for short-term wind power forecasting based on improved artificial neural network using particle swarm optimization and genetic algorithms. Energies 13, 2873 (2020)
Kim, Y., Hur, J.: An ensemble forecasting model of wind power outputs based on improved statistical approaches. Energies 13, 1071 (2020)
Cui, M., Zhang, J., Wang, Q., Krishnan, V., Hodge, B.M.: A data-driven methodology for probabilistic wind power ramp forecasting. IEEE Trans. Smart Grid 10, 1326–1338 (2017)
Zhang, Z., Sun, Y., Gao, D., Lin, J., Cheng, L.: A versatile probability distribution model for wind power forecast errors and its application in economic dispatch. IEEE Trans. Power Syst. 28, 3114–3125 (2013)
Chen, N., Qian, Z., Nabney, I.T., Meng, X.: Wind power forecasts using Gaussian processes and numerical weather prediction. IEEE Trans. Power Syst. 29, 656–665 (2014)
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.
Tsikalakis, A., Katsigiannis, Y., Georgilakis, P., Hatziargyriou, N.: Impact of wind power forecasting error bias on the economic operation of autonomous power systems. Wind Energy 12, 315–331 (2009)
Sun, Z., Zhao, M.: Short-term wind power forecasting based on VMD decomposition, conv LSTM networks and error analysis. IEEE Access 8, 134422–134434 (2020)
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
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
Majid, A.: The evaluation of wind energy based on the inherent nature of wind speed assessment at Fujairah (UAE). Instrumentation Mesure Métrologie 20(3), 121–130 (2021)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Abdul Majid, A.J. Wind energy forecasting by fitting predicted probability density functions of wind speed measurements. Int J Energy Environ Eng 13, 573–585 (2022). https://doi.org/10.1007/s40095-022-00475-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40095-022-00475-8