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[해외논문] Short-Term Direct Probability Prediction Model of Wind Power Based on Improved Natural Gradient Boosting 원문보기

Energies, v.13 no.18, 2020년, pp.4629 -   

Li, Yonggang (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China) ,  Wang, Yue (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China) ,  Wu, Binyuan (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

Abstract AI-Helper 아이콘AI-Helper

Wind energy has been widely used in renewable energy systems. A probabilistic prediction that can provide uncertainty information is the key to solving this problem. In this paper, a short-term direct probabilistic prediction model of wind power is proposed. First, the initial data set is preprocess...

참고문헌 (41)

  1. Huo Optimal real-time scheduling of wind integrated power system presented with storage and wind forecast uncertainties Energies 2015 10.3390/en8021080 8 1080 

  2. Zuluaga Short-term wind speed prediction based on robust Kalman filtering: An experimental comparison Appl. Energy 2015 10.1016/j.apenergy.2015.07.043 156 321 

  3. Neeraj A novel and alternative approach for direct and indirect wind-power prediction methods Energies 2018 10.3390/en11112923 11 2923 

  4. Costa A review on the young history of the wind power short-term prediction Renew. Sustain. Energy Rev. 2008 10.1016/j.rser.2007.01.015 12 1725 

  5. Xie Short-term patio-temporal wind power forecast in robust look-ahead power system dispatch IEEE Trans. Smart Grid 2013 10.1109/TSG.2013.2282300 5 511 

  6. Buhan Wind pattern recognition and reference wind mast data correlations with NWP for improved wind-electric power forecasts IEEE Trans. Ind. Inform. 2016 10.1109/TII.2016.2543004 12 991 

  7. Wei Ultra-short-term/short-term wind power continuous prediction based on fuzzy clustering analysis IEEE PES Innov. Smart Grid Technol. 2012 7 6 

  8. Sharma Wind power scenario generation and reduction in stochastic programming framework Electr. Power Compon. Syst. 2013 10.1080/15325008.2012.742942 41 271 

  9. Ambach A selection of time series models for short- to medium-term wind power forecasting J. Wind Eng. Ind. Aerodyn. 2015 10.1016/j.jweia.2014.11.014 136 201 

  10. 10.3390/en11040705 Zhu, Q., Chen, J., Zhu, L., Duan, X., and Liu, Y. (2018). Wind Speed Prediction with Spatio-temporal Correlation: A Deep Learning Approach. Energies, 11. 

  11. Zheng Raw wind data preprocessing: A data-mining approach IEEE Trans. Sustain. Energy 2015 10.1109/TSTE.2014.2355837 6 11 

  12. Khodayar Spatio-temporal graph deep neural network for short-term wind speed forecasting IEEE Trans. Sustain. Energy 2019 10.1109/TSTE.2018.2844102 10 670 

  13. Foley Current methods and advances in forecasting of wind power generation Renew. Energy 2012 10.1016/j.renene.2011.05.033 37 1 

  14. Poncela Automatic tuning of Kalman filters by maximum likelihood methods for wind energy forecasting Appl. Energy 2013 10.1016/j.apenergy.2013.03.041 108 349 

  15. Zhang Wind energy prediction with LS-SVM based on Lorenz perturbation J. Eng. 2017 13 1724 

  16. Villacorta Forecasting natural gas consumption using ARIMA models and artificial neural networks IEEE Latin Am. Trans. 2016 10.1109/TLA.2016.7530418 14 2233 

  17. Zhu Learning Temporal and Spatial Correlations Jointly: A Unified Framework for Wind Speed Prediction IEEE Trans. Sustain. Energy 2020 10.1109/TSTE.2019.2897136 11 509 

  18. Yang Probability interval prediction of wind power based on kde method with rough sets and weighted markov Chain IEEE Access 2018 10.1109/ACCESS.2018.2870430 6 51556 

  19. Yu Scene learning: Deep convolutional networks for wind power prediction by embedding turbines into grid space Appl. Energy 2019 10.1016/j.apenergy.2019.01.010 238 249 

  20. Yan Uncertainty estimation for wind energy conversion by probabilistic wind turbine power curve modeling Appl. Energy 2019 10.1016/j.apenergy.2019.01.180 239 1356 

  21. Wan Probabilistic forecasting of wind power generation using extreme learning machine IEEE Trans. Power Syst. 2014 10.1109/TPWRS.2013.2287871 29 1033 

  22. Naik Prediction interval forecasting of wind speed and wind power using modes decomposition based low rank multi-kernel ridge regression Renew. Energy 2018 10.1016/j.renene.2018.05.031 129 357 

  23. Cui A copula-based conditional probabilistic forecast model for wind power ramps IEEE Trans. Smart Grid 2018 1 13 

  24. Malvoni Forecasting of PV Power Generation using weather input data-preprocessing techniques Energy Procedia 2017 10.1016/j.egypro.2017.08.293 126 651 

  25. Zhang An advanced approach for construction of optimal wind power prediction intervals IEEE Trans. Power Syst. 2015 10.1109/TPWRS.2014.2363873 30 2706 

  26. Kou Probabilistic wind power forecasting with online model selection and warped gaussian process Energy Convers. Manag. 2014 10.1016/j.enconman.2014.04.051 84 649 

  27. Hu Research on wind power prediction method based on improved AdaBoost. RT and KELM Power Grid Technol. 2017 41 536 

  28. He Probe into application of Ada-BP neural network improved algorithm in electric power load forecasting Shanxi Electr. Power 2012 40 21 

  29. Liu Load forecasting of distribution network based on k-adaboost data mining Zhejiang Electr. Power 2019 38 104 

  30. Tan Ultra-short-term photovoltaic power forecasting in microgrid based on adaboost clustering Autom. Electr. Power Syst. 2017 41 33 

  31. Xie BOA-GBDT photovoltaic output prediction based on fine-grained features Power Grid Technol. 2020 44 689 

  32. Liu Short-term bus load forecast based on the fusion of XGBoost and Stacking model Electr. Power Autom. Equip. 2020 40 147 

  33. Tony, D., Anand, A., and Daisy, Y.D. (2019). NGBoost: Natural gradient boosting for probabilistic prediction. arXiv. 

  34. Zhang Ultra-short-term wind power prediction model based on modified grey model method for power control in wind farm Wind Energy 2011 35 55 

  35. 10.3390/en11112976 Han, Q., Wu, H., Hu, T., and Chu, F. (2018). Short-term wind speed forecasting based on signal decomposing algorithm and hybrid linear/nonlinear models. Energies, 11. 

  36. 10.3390/en11081975 Dong, W., Yang, Q., and Fang, X.L. (2018). Multi-step ahead wind power generation prediction based on hybrid machine learning techniques. Energies, 11. 

  37. Friedman Greedy function approximation: A gradient boosting machine Ann. Stat. 2001 10.1214/aos/1013203451 29 1189 

  38. 10.3390/en11071752 Zhou, J., Sun, N., Jia, B., and Peng, T. (2018). A novel decomposition-optimization model for short-term wind speed forecasting. Energies, 11. 

  39. Meng A novel multi-objective optimal approach for wind power interval prediction Energies 2017 10.3390/en10040419 10 419 

  40. Huang An insight into extreme learning machines: Random neurons, Random features and kernels Cogn. Comput. 2014 10.1007/s12559-014-9255-2 6 376 

  41. Yang Probabilistic interval prediction of wind power combination based on Naive Bayes High Volt. Technol. 2020 46 1099 

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