[1] |
编辑部. 新时代可再生能源绘出“新航标” [J]. 中国能源, 2021, 43(4): 1-2. DOI: 10.3969/j.issn.1003-2355.2021.04.001.
Editorial Department. Editorial department renewable energy in the new era draws a "new navigation mark" [J]. Energy of China, 2021, 43(4): 1-2. DOI: 10.3969/j.issn.1003-2355.2021.04.001. |
[2] |
陈正洪, 许杨, 许沛华, 等. 风电功率预测预报技术原理及其业务系统 [M]. 北京: 气象出版社, 2013.
CHEN Z H, XU Y, XU P H, et al. Technical principle and operational system of wind power forecasting [M] Beijing: China Meteorological Press, 2013. |
[3] |
许杨, 陈正洪, 杨宏青, 等. 风电场风电功率短期预报方法比较 [J]. 应用气象学报, 2013, 24(5): 625-630. DOI: 10.3969/j.issn.1001-7313.2013.05.012.
XU Y, CHEN Z H, YANG H Q, et al. Comparison of short-term forecast method of wind power in wind farm [J]. Journal of applied meteorological science, 2013, 24(5): 625-630. DOI: 10.3969/j.issn.1001-7313.2013.05.012. |
[4] |
冯泽深, 赵增海, 郭雁珩, 等. 2021年中国风电发展现状与展望 [J]. 水力发电, 2022, 48(10): 1-3,8. DOI: 10.3969/j.issn.0559-9342.2022.10.001.
FENG Z S, ZHAO Z H, GUO Y H, et al. Status and prospect of China's wind power development in 2021 [J]. Water power, 2022, 48(10): 1-3,8. DOI: 10.3969/j.issn.0559-9342.2022.10.001. |
[5] |
雷旭, 马鹏飞, 宋智帅, 等. 计及风电预测误差的柔性负荷日内调度模型 [J]. 发电技术, 2022, 43(3): 485-491. DOI: 10.12096/j.2096-4528.pgt.20083.
LEI X, MA P F, SONG Z H, et al. A flexible intraday load dispatch model considering wind power prediction errors [J]. Power generation technology, 2022, 43(3): 485-491. DOI: 10.12096/j.2096-4528.pgt.20083. |
[6] |
KLEIST D T, PARRISH D F, DERBER J C, et al. Introduction of the GSI into the NCEP global data assimilation system [J]. Weather and forecasting, 2009, 24(6): 1691-1705. DOI: 10.1175/ 2009WAF2222201.1. |
[7] |
JUNG J, BROADWATER R P. Current status and future advances for wind speed and power forecasting [J]. Renewable and sustainable energy reviews, 2014, 31: 762-777. DOI: 10.1016/ j.rser.2013.12.054. |
[8] |
LANGE M, FOCKEN U. Physical approach to short-term wind power prediction [M]. Berlin: Springer, 2006. DOI: 10.1007/3-540-31106-8. |
[9] |
SHCHEPETKIN A F, MCWILLIAMS J C. The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model [J]. Ocean modelling, 2005, 9(4): 347-404. DOI: 10.1016/j.ocemod.2004.08.002. |
[10] |
LIU H, CHEN C, LÜ X W, et al. Deterministic wind energy forecasting: a review of intelligent predictors and auxiliary methods [J]. Energy conversion and management, 2019, 195: 328-345. DOI: 10.1016/j.enconman.2019.05.020. |
[11] |
JIA X D, JIN C, BUZZA M, et al. Wind turbine performance degradation assessment based on a novel similarity metric for machine performance curves [J]. Renewable energy, 2016, 99: 1191-1201. DOI: 10.1016/j.renene.2016.08.018. |
[12] |
XU P H, ZHANG M Y, CHEN Z H, et al. A deep learning framework for day ahead wind power short-term prediction [J]. Applied sciences, 2023, 13(6): 4042. DOI: 10.3390/app1306 4042. |
[13] |
MA L, LUAN S Y, JIANG C W, et al. A review on the forecasting of wind speed and generated power [J]. Renewable and sustainable energy reviews, 2009, 13(4): 915-920. DOI: 10.1016/J.RSER.2008.02.002. |
[14] |
SOMAN S S, ZAREIPOUR H, MALIK O, et al. A review of wind power and wind speed forecasting methods with different time horizons [C]//Anon. North American Power Symposium, Arlington, TX, USA, September 26-28, 2010. Arlington: IEEE, 2010: 1-8. DOI: 10.1109/NAPS.2010.5619586. |
[15] |
KISVARI A, LIN Z, LIU X L. Wind power forecasting – a data-driven method along with gated recurrent neural network [J]. Renewable energy, 2021, 163: 1895-1909. DOI: 10.1016/j.renene.2020.10.119. |
[16] |
LEA C, VIDAL R, REITER A, et al. Temporal convolutional networks: a unified approach to action segmentation [C]//Anon. European Conference on Computer Vision, Amsterdam, The Netherlands, October 8-10 and 15-16, 2016. Amsterdam: Springer, 2016: 47-54. DOI: 10.1007/978-3-319-49409-8_7. |
[17] |
VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need [C]//Anon. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA, December 4-9, 2017. Long Beach: Curran Associates Inc. , 2017: 6000-6010. |
[18] |
SHAFIEE M, FINKELSTEIN M, BÉRENGUER C. An opportunistic condition-based maintenance policy for offshore wind turbine blades subjected to degradation and environmental shocks [J]. Reliability engineering & system safety, 2015, 142: 463-471. DOI: 10.1016/j.ress.2015.05.001. |
[19] |
LIM B, ARIK S O, LOEFF N, et al. Temporal fusion transformers for interpretable multi-horizon time series forecasting [EB/OL]. (2020-09-27) [2023-11-04]. http://arxiv.org/abs/1912.09363. |
[20] |
ZHENG L, HU W, MIN Y. Raw wind data preprocessing: a data-mining approach [J]. IEEE transactions on sustainable energy, 2015, 6(1): 11-19. DOI: 10.1109/TSTE.2014.2355837. |
[21] |
曹立新, 刘伟民, 郭虎全. 风电场功率曲线异常数据的清洗与建模 [J]. 兰州理工大学学报, 2022, 48(4): 64-70.
CAO L X, LIU W M,GUO H Q. Cleaning and modeling of abnormal data of wind farm power curve [J]. Journal of Lanzhou University of Technology, 2022, 48(4): 64-70. |
[22] |
ALI M, PRASAD R. Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition [J]. Renewable and sustainable energy reviews, 2019, 104: 281-295. DOI: 10.1016/j.rser.2019.01.014. |
[23] |
SEVLIAN R, RAJAGOPAL R. Detection and statistics of wind power ramps [J]. IEEE transactions on power systems, 2013, 28(4): 3610-3620. DOI: 10.1109/TPWRS.2013.2266378. |
[24] |
许沛华, 陈正洪, 孙延维, 等. 湖北山区复杂地形条件下风电功率预报算法研究 [J]. 干旱气象, 2021, 39(3): 524-532. DOI: 10.11755/j.issn.1006-7639(2021)-03-0524.
XU P H, CHEN Z H, SUN Y W, et al. Research on wind power prediction algorithm under complicated terrain in mountainous area of Hubei Province [J]. Journal of arid meteorology, 2021, 39(3): 524-532. DOI: 10.11755/j.issn.1006-7639(2021)-03-0524. |