[1] HAN F J, WANG X H, QIAO J, et al. Review on artificial intelligence based load forecasting research for the new-type power system[J]. Proceedings of the CSEE, 2023, 43(22): 8569–8591, in Chinese with English abstract, https://doi.org/10.13334/j.0258-8013.pcsee.221560
[2] National Energy Administration. National Energy Administration 2023 National Power Industry Statistics[EB/OL]. [Available at: https://www.nea.gov.cn/2024-01/26/c_1310762246.htm, 2024-01-26/2024-02-02]
[3] JIN Q S, WANG X, NI Y L, et al. Development research and outlook on photovoltaic industry under carbon peaking and carbon neutrality goals[J]. Environmental Protection, 2022, 50(1): 44–50, in Chinese with English abstract.
[4] China Electric Power Planning & Engineering Institute. Report on China Energy Development 2023[R]. Beijing: China Electric Power Planning & Engineering Institute, 2023.
[5] SINGLA P, DUHAN M, SAROHA S. A comprehensive review and analysis of solar forecasting techniques[J]. Frontiers in Energy, 2022, 16(25): 187–223, https://doi.org/10.1007/s11708-021-0722-7
[6] PARDEEP S, MANOJ D, SUMIT S. A point and interval forecasting of solar irradiance using different decomposition based hybrid models[J]. Earth Science Informatics, 2023, 16(30): 2223–2240, https://doi.org/10.1007/S12145-023-01020-9
[7] HUANG Q, QIAN Y. Comparative analysis of singlemoment and double-moment microphysics schemes in WRF on the heavy precipitation process of the macroscale and microscale characteristics of the cloud[J]. Transactions of Atmospheric Sciences, 2021, 44(4): 615–625, in Chinese with English abstract.
[8] BOUGEAULT P, TOTH Z, BISHOP C, et al. The THORPEX interactive grand global ensemble[J]. Bulletin of the American Meteorological Society, 2010, 91(8): 1059–1072, https://doi.org/10.1175/2010BAMS2853.1
[9] ZHU Y J. Ensemble forecast: A new approach to uncertainty and predictability[J]. Advances in Atmospheric Sciences, 2005, 22(6): 781–788, https://doi.org/10.1007/BF02918678
[10] RAHIMI N, PARK S, CHOI W, et al. A comprehensive review on ensemble solar power forecasting algorithms[J]. Journal of Electrical Engineering & Technology, 2023, 18: 719–733, https://doi.org/10.1007/s42835-023-01378-2
[11] ZHI X F, JI X D, ZHANG J. Multimodel ensemble forecasts of surface air temperature and precipitation over China by using Kalman filler[J]. Transactions of Atmospheric Sciences, 2019, 42(2): 197–206, in Chinese with English abstract.
[12] ZHOU H, QIN H, JI L Y, et al. Research progresses of multimodel ensemble forecast of surface meteorological elements[J]. Transactions of Atmospheric Sciences, 2022, 45(6): 815–825, in Chinese with English abstract.
[13] WEI G F, LIU H J, WU Q S, et al. Multi-model consensus forecasting technology with optimal weight for precipitation intensity levels[J]. Journal of Applied Meteorological Science, 2022, 31(6): 668–680, in Chinese with English abstract.
[14] SHENG C Y, FAN S D, RONG Y M, et al. Comparison of several objective methods and optimal consensus forecast study of temperature[J]. Meteorological Monthly, 2020, 46 (10): 1351–1361, in Chinese with English abstract.
[15] TONG H, ZHANG Y T, QI Q Q, et al. The multi-model blending forecasts of near-surface parameters based on CMA model system[J]. Meteorological Monthly, 2022, 48(12): 1539–1549, in Chinese with English abstract.
[16] WU B Y, ZHI X F, CHEN C H, et al. Multi-model ensemble forecasts of wind over East China by using augmented complex extended Kalman filter[J]. Meteorological Monthly, 2022, 48(4): 393–405, in Chinese with English abstract.
[17] ZHI X F, WU B Y, LUO Z H, et al. Multimodel ensemble forecast of high-resolution surface and high-level wind forecasts over East China[J]. Transactions of Atmospheric Sciences, 2023, 46(6): 917–927, in Chinese with English abstract.
[18] ZHAO J, WANG J Z, GUO Z H, et al. Multi-step wind speed forecasting based on numerical simulations and an optimized stochastic ensemble method[J]. Applied Energy, 2019, 255(1): 1–16, https://doi.org/10.1016/j.apenergy.2019.113833
[19] DU J, BERNER J, BUIZZA R, et al. Ensemble methods for meteorological predictions[M]// DUAN Q Y, PAPPENBERGER F, WOOD A, et al. (eds), Handbook of Hydrometeorological Ensemble Forecasting. HEIDELBERG: Springer Berlin Press, 2019: 99–149.
[20] SUN S L, WANG S Y, ZHANG G W, et al. A decomposition clustering ensemble learning approach for solar radiation forecasting[J]. Solar Energy, 2018, 163(15): 189–199, https://doi.org/10.1016/j.solener.2018.02.006
[21] GUERMOUI M, BENKACIALI S, GAIRAA K, et al. A novel ensemble learning approach for hourly global solar radiation forecasting[J]. Neural Computing and Applications, 2021, 34(4): 2983–3005, https://doi.org/10.1007/s00521-021-06421-9
[22] BAEK M K, LEE D. Spatial and temporal day-ahead total daily solar irradiation forecasting: ensemble forecasting based on the empirical biasing[J]. Energies, 2017, 11(1): 70, https://doi.org/10.3390/en11010070
[23] JIANG F, YANG J W. Short-term solar radiation forecast based on ensemble learning of multi-objective optimization[J]. Journal of Yunnan University: Natural Sciences Edition, 2021, 43(3): 451–461, in Chinese with English abstract.
[24] BASARAN K, ÖZCIFT A, KIlINC D. A new approach for prediction of solar radiation with using ensemble learning algorithm[J]. Arabian Journal for Science and Engineering, 2019, 44: 7159–7171, https://doi.org/10.1007/s13369-019-03841-7
[25] JIANG P, LIU Z K. Variable weights combined model based on multi-objective optimization for short-term wind speed forecasting[J]. Applied Soft Computing, 2019, 82: 105587, https://doi.org/10.1016/j.asoc.2019.105587
[26] CHU J C, YUAN L, PAN L, et al. NWP combination correction model based on variable-weight stacking algorithm[J]. Energy Procedia, 2019, 158: 6309–6314, https://doi.org/10.1016/j.egypro.2019.01.408
[27] State Administration for Market Regulation. Solar Energy Resource Assessment Method GB/T 37526–2019[Z]. State Administration for Market Regulation, 2019.
[28] LIU Z J, LIU B X, WANG R, et al. Research on the gale prediction methods in the Yellow Sea and Bohai Sea based on traditional and deep learning technologies[J]. Marine Forecasts, 2022, 39(6): 34–43, in Chinese with English abstract.
[29] YU X X, SONG F L, LI J, et al. Power supply security improvement of power grid with high proportion of renewable energy under extreme weather events[J]. Modern Electric Power, 2023, 40(3): 303–313, in Chinese with English abstract.
[30] DA X F, LI Z R, WANG X Y, et al. Correction technology of short-time solar radiation forecast based on cloud cover[J]. Journal of Arid Meteorology, 2021, 39(6): 1006–1016, in Chinese with English abstract.