Integrated Intelligent Energy ›› 2023, Vol. 45 ›› Issue (1): 41-48.doi: 10.3969/j.issn.2097-0706.2023.01.005
• Power System Planning • Previous Articles Next Articles
ZHENG Zhen1, ZHU Feng2, MA Xiaoli1, TIAN Shuxin2,*(), JIANG Haozhe2
Received:
2022-10-31
Revised:
2022-12-23
Published:
2023-01-25
Supported by:
CLC Number:
ZHENG Zhen, ZHU Feng, MA Xiaoli, TIAN Shuxin, JIANG Haozhe. Short-term new energy power prediction based on TL-LSTM[J]. Integrated Intelligent Energy, 2023, 45(1): 41-48.
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[1] | 黄伟, 刘琦, 杨舒文, 等. 基于主动配电系统供电能力的安全态势感知方法[J]. 电力自动化设备, 2017, 37(8):74-80. |
HUANG Wei, LIU Qi, YANG Shuwen, et al. Security situational awareness approach based on power supply capacity of active distribution systems[J]. Electric Power Automation Equipment, 2017, 37(8):74-80. | |
[2] |
王守相, 葛磊蛟. 主动配电系统运行与控制关键技术[J]. 电力建设, 2015, 36(1):85-90.
doi: 10.3969/j.issn.1000-7229.2015.01.013 |
WANG Shouxiang, GE Leijiao. Key technology of operation and control of active distribution system[J]. Electric Power Construction, 2015, 36(1):85-90.
doi: 10.3969/j.issn.1000-7229.2015.01.013 |
|
[3] | 王守相, 梁栋, 葛磊蛟. 智能配电网态势感知和态势利导关键技术[J]. 电力系统自动化, 2016, 40(12):2-8. |
WANG Shouxiang, LIANG Dong, GE Leijiao, et al. Smart distribution network situational awareness and situational leverage key technologies[J]. Automation of Electric Power Systems, 2016, 40(12):2-8. | |
[4] | 葛磊蛟, 李元良, 陈艳波, 等. 智能配电网态势感知关键技术及实施效果评价[J]. 高电压技术, 2021, 47(7):2269-2280. |
GE Leijiao, LI Yuanliang, CHEN Yanbo, et al. Key technologies of situation awareness and implementation effectiveness evaluation in smart distribution network[J]. High Voltage Engineering, 2021, 47(7):2269-2280. | |
[5] |
GEP B, DA P. Distribution of residual autocorrelations in autoregressive-integrated moving average time series models[J]. Journal of the American statistical Association, 1970, 65(332):1509-1526.
doi: 10.1080/01621459.1970.10481180 |
[6] |
HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9:1735-1780.
doi: 10.1162/neco.1997.9.8.1735 pmid: 9377276 |
[7] | CHO K, MERRIËNBOER B V, GULCEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[C]// The Association for Computational Linguistics,Conference on Empirical Methods in Natural Language Processing, 2014. |
[8] | 王粟, 江鑫, 曾亮, 等. 基于VMD-DESN-MSGP模型的超短期光伏功率预测[J]. 电网技术, 2020, 44(3):917-926. |
WANG Su, JIANG Xin, ZENG Liang, et al. Ultra-short-term photovoltaic power prediction based on VMD-DESN-MSGP model[J]. Power System Technology, 2020, 44(3):917-926. | |
[9] | 周楠, 徐潇源, 严正, 等. 基于宽度学习系统的光伏发电功率超短期预测[J]. 电力系统自动化, 2021, 45(1):55-64. |
ZHOU Nan, XU Xiaoyan, YAN Zheng, et al. Ultra-short-term prediction of photovoltaic power based on width learning system[J]. Automation of Electric Power Systems, 2021, 45(1):55-64. | |
[10] | 游坤奇, 熊殷, 贾永青, 等. 基于PCC-RBF网络的风电功率短期预测方法[J]. 电机与控制应用, 2021, 48(1):41-45,104. |
YOU Kunqi, XIONG Yin, JIA Yongqing, et al. Short-term wind power forecast method based on pearson correlation coefficient and RBF network[J]. Electric Machines and Control Application, 2021, 48(1):41-45, 104. | |
[11] | 杨晶显, 张帅, 刘继春, 等. 基于VMD和双重注意力机制LSTM的短期光伏功率预测[J]. 电力系统自动化, 2021, 45(3):174-182. |
YANG Jingxian, ZHANG Shuai, LIU Jichun, et al. Short-term PV power prediction based on VMD and dual attention mechanism LSTM[J]. Automation of Electric Power Systems, 2021, 45(3):174-182. | |
[12] | 吉锌格, 李慧, 刘思嘉, 等. 基于MIE-LSTM的短期光伏功率预测[J]. 电力系统保护与控制, 2020, 48(7):50-57. |
JI Xinge, LI Hui, LIU Sijia, et al. Short-term photovoltaic power forecasting based on MIE-LSTM[J]. Power System Protection and Control, 2020, 48(7):50-57. | |
[13] | 曹力, 潘巧波, 王明宇, 等. 基于混合核函数支持向量机的风电机组发电机温度预警方法[J]. 华电技术, 2020, 42(5):43-49. |
CAO Li, PAN Qiaobo, WANG Mingyu, et al. Early warning method for wind turbine generator temperature based on HK-SVM[J]. Huadian Technology, 2020, 42(5):43-49. | |
[14] | 杨明明. 基于卷积神经网络的机舱风速修正[J]. 华电技术, 2021, 43(5):75-79. |
YANG Mingming. Wind speed correction for wind turbine based on convolutional neural network[J]. Huadian Technology, 2021, 43(5):75-79. | |
[15] | 韩义, 张奇月, 王研凯, 等. 基于BP神经网络的300 MW循环流化床机组出力预测[J]. 华电技术, 2020, 42(12):1-6. |
HAN Yi, ZHANG Qiyue, WANG Yankai, et al. Performance prediction on a 300 MW CFB based on BP neural network[J]. Huadian Technology, 2020, 42(12):1-6. | |
[16] | 杨毅, 范栋琛, 殷浩然, 等. 基于深度-迁移学习的输电线路故障选相模型及其可迁移性研究[J]. 电力自动化设备, 2020, 40(10):165-172. |
YANG Yi, FAN Dongchen, YIN Haoran, et al. Deep-migration learning based transmission line fault phase selection model and its migrability study[J]. Electric Power Automation Equipment, 2020, 40(10):165-172. | |
[17] | 孙晓燕, 李家钊, 曾博, 等. 基于特征迁移学习的综合能源系统小样本日前电力负荷预测[J]. 控制理论与应用, 2021, 38(1):63-72. |
SUN Xiaoyan, LI Jiazhao, ZENG Bo, et al. Small-sample day-ahead power load forecasting of integrated energy system based on feature transfer learning[J]. Control Theory and Applications, 2021, 38(1):63-72. | |
[18] | 王伟胜, 王铮, 董存, 等. 中国短期风电功率预测技术现状与误差分析[J]. 电力系统自动化, 2021, 45(1):17-27. |
WANG Weisheng, WANG Zheng, DONG Cun, et al. Current status and error analysis of short-term wind power forecasting technology in China[J]. Automation of Electric Power System, 2021, 45(1):17-27. | |
[19] | LIU F, TING K, ZHOU Z, et al. Isolation forest[C]// Institute of Electrical and Electronics Engineers.Eighth IEEE International Conference on Data Mining, 2008:413-422. |
[20] | PAPARRIZOS J, LUIS G. K-Shape:Efficient and accurate clustering of time series[J]. Sigmod Record, 2016, 45:69-76. |
[21] |
ELMAN J. Finding structure in time[J]. Cognitive Science, 1990, 14(2):179-211.
doi: 10.1207/s15516709cog1402_1 |
[22] | 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):4859-4870. |
YU Guangzheng, LU Liu, TANG Bo, 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):4859-4870. | |
[23] | YAO Q, SONG D, CHEN H, et al. A dual-stage attention-based recurrent neural network for time series prediction[C]// IJCAI Inc.International Joint Conference on Artificial Intelligence, 2017:2627-2623. |
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