SHORT-TERM ZONING POWER PREDICTION OF OFFSHORE WIND POWER CONSIDERING CLIMBING FEATURE QUANTITIES

Shi Shuai; Zhang Hao; Huang Dongmei; Li Yuanyuan; Mi Yang; Yang Xiaodong

doi:10.19912/j.0254-0096.tynxb.2023-1292

PDF(3647 KB)

Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (12) : 258-268. DOI: 10.19912/j.0254-0096.tynxb.2023-1292

SHORT-TERM ZONING POWER PREDICTION OF OFFSHORE WIND POWER CONSIDERING CLIMBING FEATURE QUANTITIES

Shi Shuai¹, Zhang Hao¹, Huang Dongmei², Li Yuanyuan¹, Mi Yang¹, Yang Xiaodong³

Author information +

History +

Abstract

Considering complex marine conditions, this paper proposes a zonal hybrid prediction model considering climbing features quantities. Firstly, the improved bump detection technology is used to identify and divide the power fluctuation period. Secondly, this paper comprehensively considers the power fluctuation characteristics of different typical meteorological days to classify the meteorological data. Thirdly, considering the power volatility of wind power, this paper proposes a hybrid prediction model, which adopts a LightGBM-LSTM point prediction model in the power discontinuous fluctuation section and an RF-LSTM interval prediction model in the power continuous fluctuation period, and obtains a good prediction effect. Finally, the data of an offshore wind farm in eastern China is selected for improved simulation and case analysis. The results show that compared with the traditional wind power point prediction and interval prediction methods, the prediction accuracy of the partition hybrid prediction model considering wind power climbing and meteorological daily classification proposed in this paper is significantly improved.

Key words

climbing feature quantities / wind power climbing events / bump event detection / meteorological day classification / combination model / partition forecasting

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Shi Shuai, Zhang Hao, Huang Dongmei, Li Yuanyuan, Mi Yang, Yang Xiaodong. SHORT-TERM ZONING POWER PREDICTION OF OFFSHORE WIND POWER CONSIDERING CLIMBING FEATURE QUANTITIES[J]. Acta Energiae Solaris Sinica. 2024, 45(12): 258-268 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1292

References

[1] 王勃, 刘晓琳. 计及多误差场景集划分的超短期NWP风速修正方法[J]. 南方电网技术, 2023, 17(2): 118-127, 136.
WANG B, LIU X L.Ultra-short-term NWP wind speed correction method based on multiple error scenarios division[J]. Southern power system technology, 2023, 17(2): 118-127, 136.
[2] YAN J, OUYANG T H.Advanced wind power prediction based on data-driven error correction[J]. Energy conversion and management, 2019, 180: 302-311.
[3] DEMOLLI H, DOKUZ A S, ECEMIS A, et al.Wind power forecasting based on daily wind speed data using machine learning algorithms[J]. Energy conversion and management, 2019, 198: 111823.
[4] HAO J, ZHU C S, GUO X T.Wind power short-term forecasting model based on the hierarchical output power and Poisson re-sampling random forest algorithm[J]. IEEE access, 2020, 9: 6478-6487.
[5] HOSSAIN M A, CHAKRABORTTY R K, ELSAWAH S, et al.Predicting wind power generation using hybrid deep learning with optimization[J]. IEEE transactions on applied superconductivity, 2021, 31(8): 0601305.
[6] ZHANG H, LIU Y Q, YAN J, et al.Improved deep mixture density network for regional wind power probabilistic forecasting[J]. IEEE transactions on power systems, 2020, 35(4): 2549-2560.
[7] SUN Z X, ZHAO M Y.Short-term wind power forecasting based on VMD decomposition, ConvLSTM networks and error analysis[J]. IEEE access, 2020, 8: 134422-134434.
[8] DOLATABADI A, ABDELTAWAB H, MOHAMED Y A R I. Hybrid deep learning-based model for wind speed forecasting based on DWPT and bidirectional LSTM network[J]. IEEE access, 2020, 8: 229219-229232.
[9] 雷柯松, 吐松江·卡日, 伊力哈木·亚尔买买提, 等. 基于WGAN-GP和CNN-LSTM-Attention的短期光伏功率预测[J]. 电力系统保护与控制, 2023, 51(9): 108-118.
LEI K S, TUSONGJIANG·KARI, YILIHAMU·YAERMAIMAITI, et al. Prediction of short-term photovoltaic power based on WGAN-GP and CNN-LSTM-Attention[J]. Power system protection and control, 2023, 51(9): 108-118.
[10] 苏连成, 朱娇娇, 李英伟. 基于时间卷积网络残差校正的短期风电功率预测[J]. 太阳能学报, 2023, 44(7): 427-435.
SU L C, ZHU J J, LI Y W.Short-term wind power prediction based on temporal convolutional network residual correction model[J]. Acta energiae solaris sinica, 2023, 44(7): 427-435.
[11] 栗然, 马涛, 张潇, 等. 基于卷积长短期记忆神经网络的短期风功率预测[J]. 太阳能学报, 2021, 42(6): 304-311.
LI R, MA T, ZHANG X, et al.Short-term wind power prediction based on convolutional long-short-term memory neural networks[J]. Acta energiae solaris sinica, 2021, 42(6): 304-311.
[12] ZOU M Z, HOLJEVAC N, ĐAKOVIĆ J, et al.Bayesian CNN-BiLSTM and vine-GMCM based probabilistic forecasting of hour-ahead wind farm power outputs[J]. IEEE transactions on sustainable energy, 2022, 13(2): 1169-1187.
[13] 黄棋悦, 严楠, 钟旭佳. 基于生成对抗网络的风电爬坡功率预测[J]. 太阳能学报, 2023, 44(1): 226-231.
HUANG Q Y, YAN N, ZHONG X J.Wind power ramping events prediction based on generative adversarial network[J]. Acta energiae solaris sinica, 2023, 44(1): 226-231.
[14] KO M S, LEE K, KIM J K, et al.Deep concatenated residual network with bidirectional LSTM for one-hour-ahead wind power forecasting[J]. IEEE transactions on sustainable energy, 2021, 12(2): 1321-1335.
[15] 张淑清, 杜灵韵, 王册浩, 等. 基于格拉姆角场与改进CNN-ResNet的风电功率预测方法[J]. 电网技术, 2023, 47(4): 1540-1548.
ZHANG S Q, DU L Y, WANG C H, et al.Wind power forecasting method based on GAF and improved CNN-Res Net[J]. Power system technology, 2023, 47(4): 1540-1548.
[16] HE Y Y, ZHANG W Y.Probability density forecasting of wind power based on multi-core parallel quantile regression neural network[J]. Knowledge-based systems, 2020, 209: 106431.
[17] 杨茂, 董昊. 基于数值天气预报风速和蒙特卡洛法的短期风电功率区间预测[J]. 电力系统自动化, 2021, 45(5): 79-85.
YANG M, DONG H.Short-term wind power interval prediction based on wind speed of numerical weather prediction and Monte Carlo method[J]. Automation of electric power systems, 2021, 45(5): 79-85.
[18] ZHANG H, YAN J, LIU Y Q, et al.Multi-source and temporal attention network for probabilistic wind power prediction[J]. IEEE transactions on sustainable energy, 2021, 12(4): 2205-2218.
[19] 赵永宁, 李卓, 叶林, 等. 基于时空相关性的风电功率超短期自适应预测方法[J]. 电力系统保护与控制, 2023, 51(6): 94-105.
ZHAO Y N, LI Z, YE L, et al.A very short-term adaptive wind power forecasting method based on spatio-temporal correlation[J]. Power system protection and control, 2023, 51(6): 94-105.
[20] 胡文慧, 苏欣, 姜林, 等. 考虑预测误差时空相依性的短期风电功率概率预测[J]. 南方电网技术, 2023, 17(2): 137-144.
HU W H, SU X, JIANG L, et al.Probabilistic prediction of short-term wind power considering the temporal and spatial dependence of prediction errors[J]. Southern power system technology, 2023, 17(2): 137-144.
[21] 黎博, 陈民铀, 钟海旺, 等. 高比例可再生能源新型电力系统长期规划综述[J]. 中国电机工程学报, 2023, 43(2): 555-581.
LI B, CHEN M Y, ZHONG H W, et al.A review of long-term planning of new power systems with large share of renewable energy[J]. Proceedings of the CSEE, 2023, 43(2): 555-581.
[22] 熊予涵, 彭小圣, 杨子民, 等. 基于参数自适应旋转门和Bump事件筛选的风电爬坡事件识别[J]. 南方电网技术, 2023, 17(2): 101-110.
XIONG Y H, PENG X S, YANG Z M, et al.Identification of wind power ramp events based on parameter adaptive swinging door and bump event selection[J]. Southern power system technology, 2023, 17(2): 101-110.
[23] 余光正, 陆柳, 汤波, 等. 考虑转折性天气的海上风电功率超短期分段预测方法研究[J]. 中国电机工程学报, 2022, 42(13): 4859-4871.
YU G Z, LU L, TANG B, et al.Research on ultra-short-term subsection forecasting method of offshore wind power considering transitional weather[J]. Proceedings of the CSEE, 2022, 42(13): 4859-4871.
[24] 刘雅婷, 杨明, 于一潇, 等. 基于多场景敏感气象因子优选及小样本学习与扩充的转折性天气日前风电功率预测[J]. 高电压技术, 2023, 49(7): 2972-2982.
LIU Y T, YANG M, YU Y X, et al.Transitional-weather-considered day-ahead wind power forecasting based on multi-scene sensitive meteorological factor optimization and few-shot learning[J]. High voltage engineering, 2023, 49(7): 2972-2982.
[25] 周海, 刘建锋, 周健, 等. 基于改进降噪自编码器和多元时序聚类的海上风电功率预测[J]. 太阳能学报, 2023, 44(3): 129-138.
ZHOU H, LIU J F, ZHOU J, et al.Offshore wind power prediction based on improved denoising auto-encoder and multivariate time series clustering[J]. Acta energiae solaris sinica, 2023, 44(3): 129-138.
[26] 吴浩天, 孙荣富, 廖思阳, 等. 基于改进气象聚类分型的短期风电功率概率预测方法[J]. 电力系统自动化, 2022, 46(15): 56-65.
WU H T, SUN R F, LIAO S Y, et al.Short-term wind power probability forecasting method based on improved meteorological clustering and classification[J]. Automation of electric power systems, 2022, 46(15): 56-65.
[27] ZOU F, FU W L, FANG P, et al. A hybrid model based on multi-stage principal component extraction, GRU network and KELM for multi-step short-term wind speed forecasting[J]. IEEE access, 2931, 8: 222931-222943.
[28] 杨国清, 刘世林, 王德意, 等. 基于Attention-GRU风速修正和Stacking的短期风电功率预测[J]. 太阳能学报, 2022, 43(12): 273-281.
YANG G Q, LIU S L, WANG D Y, et al.Short-term wind power forecasting based on Attention-GRU wind speed correction and stacking[J]. Acta energiae solaris sinica, 2022, 43(12): 273-281.
[29] 陈峰, 余轶, 徐敬友, 等. 基于Bayes-LSTM网络的风电出力预测方法[J]. 电力系统保护与控制, 2023, 51(6): 170-178.
CHEN F, YU Y, XU J Y, et al.Prediction method of wind power output based on a Bayes-LSTM network[J]. Power system protection and control, 2023, 51(6): 170-178.