参考文献/References:
[1] 韩自奋,景乾明,张彦凯,等.风电预测方法与新趋势综述[J].电力系统保护与控制, 2019, 47(24):178-187.
[2] Gong M J, Yan C C, Xu W, et al. Short-term wind power forecasting model based on temporal convolutional network and Informer[J]. Energy, 2023, 283.
[3] 涂思嘉,杨悦荣,林舜江,等. 考虑风电不确定性的交直流混联电网静态电压稳定优化控制方法[J]. 电力科学与技术学报, 2023, 38(3): 94-104.
[4] 张颖超,成金杰,邓华,等.基于相似日和特征提取的短期风电功率预测[J].郑州大学学报(工学版),2020,41(05):44-49.
[5] 蒋建东,孙书凯,董存,等.风电中长期电量预测研究现状[J].高电压技术, 2022, 48(02):409-419.
[6] Antonanzas J, Osorio N, Escobar R, et al. Review of photovoltaic power forecasting [J]. Solar Energy 2016; 136: 78–111 .
[7] Ahmed R, Sreeram V, Mishra Y, et al. A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization [J]. Renewable and Sustainable Energy Reviews, 2020, 124: 1-26.
[8] Soubdhan T, Ndong J, Ould Baba et al. A robust forecasting framework based on the kalman filtering approach with a twofold parameter tuning procedure: Application to solar and photovoltaic prediction [J]. Solar Energy 2016, 131: 246-259 .
[9] DE ALENCAR D B, DE MATTOS AFFONSO C, DE OLIVEIRA R C L, et al. Different models for forecasting wind power generation: case study[J]. Energies, 2017, 12(10): 1-27 .
[10] Yang D. Making reference solar forecasts with climatology, persistence, and their optimal convex combination [J]. Solar Energy 2019, 193: 981-985.
[11] Hu W, Yang C. Grey model of direct solar radiation intensity on the horizontal plane for cooling loads calculation plane for cooling loads calculation [J]. Building and Environment 2000, 35: 587–593.
[12] Wang S, Li B, Li G Z, et al. Short-term wind power prediction based on multidimensional data cleaning and feature reconfiguration[J]. Applied Energy, 2021, 292.
[13] Liu W, Liu Y M, Fu L, et al. Wind Power Forecasting Method Based on Bidirectional Long Short-Term Memory Neural Network and Error Correction[J].Electric Power Components and Systems, 2022, 49(13-14):1169-1180.
[14] Li Z, Xu R S, Luo X R, et al. Short-term wind power prediction based on modal reconstruction and CNN-BiLSTM[J]. Energy Reports, 2023, 96449-6460.
[15] 李润金,李丽霞.基于VMD-CNN-LSTM模型的短期风电功率预测[J].沈阳工程学院学报(自然科学版), 2024, 20(01):6-13.
[16] 陈申,叶小岭,熊雄等.基于天鹰优化算法的短期风电功率区间预测[J].重庆理工大学学报(自然科学), 2023, 37(04):304-314.
[17] 欧阳资生,唐伯聪.基于VMD-Bi LSTM-ATT预测模型的碳中和指数量化投资研究[J].金融经济,2023(10):75-90.
[18] 肖烈禧,张玉,周辉,等基于IAOA-VMD-LSTM的超短期风电功率预测[J].太阳能学报, 2023, 44(11):239-246.
[19] 李飞宏,肖迎群.基于VMD-GRU-EC的短期电力负荷预测方法[J].中国测试,2023,49(10):120-127.
[20] XUE J, SHEN B. Dung beetle optimizer: a new meta-heuristic algorithm for global optimization[J/OL]. The Journal of Sup ercomputing, 2023, 79(7): 7305-7336.
[21] 郭琴,郑巧仙.多策略改进的蜣螂优化算法及其应用[J/OL].计算机科学与探索:1-22[2023-12-27].
[22] Dehghani Mohammad, Trojovský Pavel. Osprey optimization algorithm: A new bio-inspired metaheuristic algorithm for solving engineering optimization problems[J]. Frontiers in Mechanical Engineering, 2023,8:1126450.
[23] 李津,史加荣,张琰妮,等.基于最大信息系数的短期太阳辐射协同估计[J].太阳能学报, 2023, 44(09):286-294.
[24] 杨锡运,刘玉奇,李建林.基于四分位法的含储能光伏电站可靠性置信区间计算方法[J].电工技术学报, 2017,32(15):136-144.
[25] 杨子民,彭小圣,熊予涵等.计及邻近风电场信息与CNN-BiLSTM的短期风电功率预测[J].南方电网技术, 2023, 17(02):47-56.
[26] 辛征,王琦,刘兴然.短期风电功率预测的深度学习模型[J].计算机时代, 2023(02):33-36+41.
[27] 赵志浩.面向手机部件的目标区域检测算法的设计与实现[D].沈阳:中国科学院大学(中国科学院沈阳计算技术研究所),2020.