Multi-model ensemble forecasting of 10-m wind speed over eastern China based on machine learning optimization

Alessandrini, 2011, An application of ensemble/multi model approach for wind power production forecast, Adv. Sci. Res., 6, 35, 10.5194/asr-6-35-2011 Bianco, 2016, A wind energy ramp tool and metric for measuring the skill of numerical weather prediction models, Wea. Forecast., 31, 1137, 10.1175/WAF-D-15-0144.1 Buizza, 1999, Stochastic representation of model uncertainties in the ECMWF ensemble prediction system, Q. J. R. Meteorol. Soc., 125, 2887, 10.1002/qj.49712556006 Cassola, 2012, Wind speed and wind energy forecast through Kalman filtering of numerical weather prediction model output, Appl. Energy, 99, 154, 10.1016/j.apenergy.2012.03.054 Fang, 2023, Short-term wind speed forecasting bias correction in the Hangzhou area of China based on a machine learning model, Atmos. Ocean. Sci. Lett., 10.1016/j.aosl.2023.100339 Feng, 2016, Fine-scale forecasting of surface temperature based on multi-model ensemble technology, Meteorol. Environ. Sci., 39, 73 Georgilakis, 2008, Technical challenges associated with the integration of wind power into power systems, Renew. Sust. Energ. Rev., 12, 852, 10.1016/j.rser.2006.10.007 Gong, 2015, Evaluation of surface meteorological elements from several numerical models in China, Clim. Environ. Res., 20, 53 Han, 2021, A deep learning method for bias correction of ECMWF 24–240h forecasts, Adv. Atmos. Sci., 38, 1444, 10.1007/s00376-021-0215-y Jung, 2019, The temporal variability of global wind energy–Long-term trends and inter-annual variability, Energy Convers. Manage., 188, 462, 10.1016/j.enconman.2019.03.072 Li, 2018, Application of partial least squares regression in multi-model ensemble forecasting of water vapor and surface temperature, Adv. Earth Sci., 33, 404 Lyu, 2022, Analyses on the multimodel wind forecasts and error decompositions over North China, Atmosphere (Basel), 13, 1652, 10.3390/atmos13101652 Makarov, 2009, Operational impacts of wind generation on California power systems, IEEE Trans. Power Syst., 24, 1039, 10.1109/TPWRS.2009.2016364 Men, 2019, A temperature correction method for multi-model ensemble forecast in North China based on machine learning, Clim. Environ. Res., 24, 116 Mehr, 2021, The validity of deep learning computational model for wind speed Simulation, 1 Özen, C., Kaplan, O., Özcan, C., Dinç, U., 2019. Short term wind speed forecast by using long short term memory. In: Proceedings of the 9th International Symposium on Atmospheric Sciences ATMOS 2019. Istanbul, Turkey, 23–26 October 2019. https://www.researchgate.net/publication/336855955_Short_Term_Wind_Speed_Forecast_By_Using_Long_Short_Term_Memory. Pedregosa, 2011, Scikit-Learn: Machine learning in Python, J. Mach. Learn. Res., 12, 2825 Peng, 2020, Prediction skill of extended range 2-m maximum air temperature probabilistic forecasts using machine learning post-processing methods, Atmosphere (Basel), 11, 823, 10.3390/atmos11080823 Rabier, 1996, Sensitivity of forecast error to initial conditions, Q. J. R. Meteorol. Soc., 122, 121, 10.1002/qj.49712252906 Ren, 2020, Error analysis and correction of short-term numerical weather prediction under complex terrain based on machine learning, Acta Meteorol. Sin., 78, 1002 Rodrigues, 2018, Deep downscale: A deep learning strategy for high-resolution weather forecast, 415 Shi, 2022, Wind speed forecasts of a mesoscale ensemble for large-scale wind farms in northern China: Downscaling effect of global model forecasts, Energies, 15, 896, 10.3390/en15030896 Shi, 2017, Application research on the multi-model fusion forecast of wind speed, Plateau Meteorol., 36, 1022 Siuta, 2018, Benefits of a multimodel ensemble for hub-height wind prediction in mountainous terrain, Wind Energy, 21, 783, 10.1002/we.2195 Sun, 2019, Adjusting wind speed prediction of numerical weather forecast model based on machine learning methods, Meteorol. Mon., 45, 426 Wang, 2023, Multi-model ensemble forecasts of surface air temperatures in Henan province based on machine learning, Atmosphere (Basel), 14, 520, 10.3390/atmos14030520 Wang, 2018, Short-term wind speed forecast based on least squares support vector machine, J. Inf. Process. Syst., 14, 1385 Wu, 2022, Multi-model ensemble forecasts of wind over East China by using augmented complex extended Kalman filter, Meteor. Mon., 48, 393 Wyszogrodzki, 2013, Analysis of the surface temperature and wind forecast errors of the NCAR-AirDat operational CONUS 4-km WRF forecasting system, Meteorol. Atmos. Phys., 122, 125, 10.1007/s00703-013-0281-5 Xu, 2020, Wind speed forecast based on post-processing of numerical weather predictions using a gradient boosting decision tree algorithm, Atmosphere (Basel), 11, 738, 10.3390/atmos11070738 Yao, 2018, Numerical simulation for wind speed at the mountain wind power farm in Jiangxi, Meteorol. Environmental Sci., 41, 120 Yu, 2016, Climatology and trend of wind power resources in China and its surrounding regions: A revisit using climate forecast system reanalysis data, Int. J. Climatol., 36, 2173, 10.1002/joc.4485 Zhao, 2016, An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed, Appl. Energy., 162, 808, 10.1016/j.apenergy.2015.10.145 Zhi, 2009, Superensemble forecasts of the surface temperature in Northern Hemisphere middle latitudes, Scientia Meteorol. Sin., 29, 569 Zhi, 2012, A Comparison of three kinds of multimodel ensemble forecast techniques based on the TIGGE data, Acta. Meteorol. Sin., 26, 41, 10.1007/s13351-012-0104-5 Zjavka, 2015, Wind speed forecast correction models using polynomial neural networks, Renew. Energy, 83, 998, 10.1016/j.renene.2015.04.054