Short-term wind speed forecasting bias correction in the Hangzhou area of China based on a machine learning model

Bergstra, 2012, Random search for hyper-parameter optimization, J. Mach. Learn. Res., 13, 281 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 Chen, 2016, XGBoost: a scalable tree boosting system, J. Assoc. Comput. Mach., 785 Deng, 2022, Bagging-XGBoost algorithm based extreme weather identification and short-term load forecasting model, Energy Rep., 8, 8661, 10.1016/j.egyr.2022.06.072 Friedman, 2001, Greedy function approximation: a gradient boosting machine, Ann. Statist., 29, 1189, 10.1214/aos/1013203451 Glahn, 1972, The use of model output statistics (MOS) in objective weather forecasting, J. Appl. Meteorol., 11, 1203, 10.1175/1520-0450(1972)011<1203:TUOMOS>2.0.CO;2 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 He, 2021, Machine learning and its potential application to climate prediction, Trans. Atmos. Sci., 44, 26 Houtekamer, 1998, Data assimilation using an ensemble kalman filter technique, Mon. Wea. Rev., 126, 796, 10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2 Klein, 1959, Objective prediction of five-day mean temperatures during winter, J. Meteorol., 16, 672, 10.1175/1520-0469(1959)016<0672:OPOFDM>2.0.CO;2 Kong, 2022, A deep spatio-temporal forecasting model for multi-site weather prediction post-processing, Commu. Compu. Phys., 31, 131, 10.4208/cicp.OA-2020-0158 Li, 2019, A model output machine learning method for grid temperature forecasts in the Beijing area, Adv. Atmos. Sci., 36, 1156, 10.1007/s00376-019-9023-z Lipton, 2018, The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery, Queue, 16, 31, 10.1145/3236386.3241340 Liu, 2022, Meshless surface wind speed field reconstruction based on machine learning, Adv. Atmos. Sci., 39, 1721, 10.1007/s00376-022-1343-8 Louppe, 2013, Understanding variable importances in forests of randomized trees, Cur. Ass. Inc., 431 McGovern, 2019, Making the black box more transparent: understanding the physical implications of machine learning, Bull. Am. Meteorol. Soc., 100, 2175, 10.1175/BAMS-D-18-0195.1 Men, 2019, A temperature correction method for multi-model ensemble forecast in North China based on machine learning, Clim. Environ. Res., 24, 116 Olah, 2017, Feature Visualization, Distill, 2, 11, 10.23915/distill.00007 Phan, 2021, A hybrid wind power forecasting model with XGBoost, data preprocessing considering different NWPs, Appl. Sci., 11, 1100, 10.3390/app11031100 Rasp, 2018, Neural networks for postprocessing ensemble weather forecasts, Mon. Wea. Rev., 146, 3885, 10.1175/MWR-D-18-0187.1 Reichstein, 2019, Deep learning and process understanding for data-driven earth system science, Nature, 566, 195, 10.1038/s41586-019-0912-1 Sun, 2019, Adjusting wind speed prediction of numerical weather forecast model based on machine learning methods, Meteorol. Mon., 45, 426 Vashani, 2010, Comparative evaluation of different post processing methods for numerical prediction of temperature forecasts over Iran, Res. J. Environ. Sci., 4, 305, 10.3923/rjes.2010.305.316 Wilczak, 2015, The wind forecast improvement project (WFIP): a public–private partnership addressing wind energy forecast needs, Bull. Am. Meteorol. Soc., 96, 1699, 10.1175/BAMS-D-14-00107.1 Wu, 2020, Application of least square method in correction and forecast of extreme wind speed in Dongtou district, J. Zhejiang Meteorol., 41, 1 Wu, 2022, Research on error correction of apparent temperature in Beijing, J. Meteor. Sci., 42, 261 Xia, 2020, Machine learning-based weather support for the 2022 Winter Olympics, Adv. Atmos. Sci., 37, 927, 10.1007/s00376-020-0043-5 Xiong, 2022, A short-term wind power forecast method via XGBoost hyper-parameters optimization, Front. Energy. Res., 10, 10.3389/fenrg.2022.905155 Yang, 2020, Research on human comfort of Hangzhou streets based on Spring microclimate effect, 314