建模、分析、优化和决策 |
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基于EMD-SC和AGSA优化支持向量机的超短期风电功率组合预测 |
江岳春, 杨旭琼, 陈礼锋, 贺飞 |
湖南大学 电气与信息工程学院, 湖南 长沙 410082 |
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Super-short-time wind power combination forecasting based on support vector machine optimized by EMD-SC and AGSA |
JIANG Yue-chun, YANG Xu-qiong, CHEN Li-feng, HE Fei |
College of Electrical and Information Engineering, Hunan University, Changsha 410082, China |
引用本文:
江岳春, 杨旭琼, 陈礼锋, 贺飞. 基于EMD-SC和AGSA优化支持向量机的超短期风电功率组合预测[J]. 工程设计学报, 2017, 24(2): 187-195.
JIANG Yue-chun, YANG Xu-qiong, CHEN Li-feng, HE Fei. Super-short-time wind power combination forecasting based on support vector machine optimized by EMD-SC and AGSA[J]. Chinese Journal of Engineering Design, 2017, 24(2): 187-195.
链接本文:
https://www.zjujournals.com/gcsjxb/CN/10.3785/j.issn.1006-754X.2017.02.010
或
https://www.zjujournals.com/gcsjxb/CN/Y2017/V24/I2/187
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[1] |
张宁宇,高山,赵欣.一种考虑风电随机性的机组组合模型及其算法[J].电工技术学报,2013,28(5):22-29. ZHANG Ning-yu, GAO Shan, ZHAO Xin. An unit commitment model and algorithm with randomness of wind power[J]. Transactions of China Electrotechnical Society, 2013, 28(5): 22-29.
|
[2] |
MA Xi-yuan, SUN Yuan-zhang, FANG Hua-liang. Scenario generation of wind power based on statistical uncertainty and variability[J]. IEEE Transaction on Sustainable Energy, 2013, 4(4): 894-904.
|
[3] |
DONG L, WANG L J, SHAHNAWAZ F K, et al. Wind power day-ahead prediction with cluster analysis of NWP[J]. Renewable and Sustainable Energy Reviews, 2016, 60(6): 1206-1212.
|
[4] |
HUANG Z X. Extensions to the K-means algorithm for clustering large data sets with categorical values[J]. Data Mining and Knowledge Discovery, 1998, 2(3):283-304.
|
[5] |
周林,平西建,徐森,等.基于谱聚类的聚类集成算法[J].自动化学报,2012,38(8):1335-1342. ZHOU Lin, PING Xi-Jian, XU Sen, et al. Cluster ensemble based on spectral clustering[J]. Acta Automatica Sinica, 2012, 38(8): 1335-1342.
|
[6] |
BACH F R, JORDAN M I. Learning spectral clustering[J]. Advances in Neural Information Processing Systems, 2004, 16(2): 1-13.
|
[7] |
PEIYUAN C, PEDERSEN T, BAK J B, et al. ARIMA-based time series model of stochastic wind power generation [J]. IEEE Transactions on Power Systems, 2010, 25(2): 667-676.
|
[8] |
LOUKA P, GALANIS G, SIEBERT N, et al. Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2008, 96(12): 2348-2362.
|
[9] |
范高峰,王伟胜,刘纯,等.基于人工神经网络的风电功率预测[J].中国电机工程学报,2008,28(34):118-123. FAN Gao-feng, WANG Wei-sheng, LIU Chun, et al. Wind power prediction based on artificial neural network[J]. Proceedings of the CSEE, 2008, 28(34): 118-123.
|
[10] |
江岳春,张丙江,邢方方,等.基于混沌时间序列GA-VNN模型的超短期风功率多步预测[J].电网技术,2015,39(8):2160-2166. JIANG Yue-chun, ZHANG Bing-jiang, XING Fang-fang, et al. Super-short-term multi-step prediction of wind power based on GA-VNN model of chaotic time series[J]. Power System Technology, 2015, 39(8): 2160-2166.
|
[11] |
杨琦,张建华,王向峰,等.基于小波-神经网络的风速及风力发电量预测[J].电网技术,2009,33(17):44-48. YANG Qi, ZHANG Jian-hua, WANG Xiang-feng, et al. Wind speed and generated wind power forecast based on wavelet-neural network[J]. Power System Technology, 2009, 33(17): 44-48.
|
[12] |
严欢,卢继平,覃俏云,等.基于多属性决策和支持向量机的风电功率非线性组合预测[J].电力系统自动化,2013,37(10):29-34. YAN Huan, LU Ji-ping, QIN Qiao-yun, et al. A nonlinear combined model for wind power forecasting based on multi-attribute decision-making and support vector machine[J]. Automation of Electric Power Systems, 2013, 37(10): 29-34.
|
[13] |
谢宏,魏江平,刘鹤立.短期负荷预测中支持向量机模型的参数选取和优化方法[J].中国电机工程学报, 2006,26(22):17-22. XIE Hong, WEI Jiang-ping, LIU He-li. Parameter selection and optimization method of SVM model for short-term load forecasting[J]. Proceedings of the CSEE, 2006, 26(22): 17-22.
|
[14] |
肖白,聂鹏,穆钢,等.基于多级聚类分析和支持向量机的空间负荷预测方法[J].电力系统自动化,2015,39(12):56-61. XIAO Bai, NIE Peng, MU Gang, et al. A spatial load forecasting method based on multilevel clustering analysis and support vector machine[J]. Automation of Electric Power Systems, 2015, 39(12): 56-61.
|
[15] |
高昆仑,刘建明,徐茹枝,等.基于支持向量机和粒子群算法的信息网络安全态势复合预测模型[J].电网技术,2011,35(4):176-182. GAO Kun-lun, LIU Jian-ming, XU Ru-zhi, et al. A hybrid security situation prediction model for information network based on support vector machine and particle swarm optimization[J]. Power System Technology, 2011, 35(4): 176-182.
|
[16] |
ZHANG W, NIU P, LI G, et al. Forecasting of turbine heat rate with online least squares support vector machine based on gravitational search algorithm[J]. Knowledge-Based Systems, 2013, 39(2): 34-44.
|
[17] |
KUANG W, YANG Z, LING W K, et al. Nonlinear and adaptive undecimated hierarchical multiresolution analysis for real valued discrete time signals via empirical mode decomposition approach[J]. Digital Signal Processing, 2015, 45(10): 36-54.
|
[18] |
蔡晓妍,戴冠中,杨黎斌.谱聚类算法综述[J].计算机科学,2008,35(7):14-18. CAI Xiao-yan, DAI Guan-zhong, YANG Li-bin. Survey on spectral clustering algorithms[J]. Computer Science, 2008, 35(7): 14-18.
|
[19] |
荣海娜,张葛祥,金炜东.系统辨识中支持向量机核函数及其参数的研究[J].系统仿真学报,2006,18(11): 3204-3208. RONG Hai-na, ZHANG Ge-xiang, JIN Wei-dong. Selection of kernel functions and parameters for support vector machines in system identification[J]. Journal of System Simulation, 2006, 18(11): 3204-3208.
|
[20] |
MOHANDES M A, HALAWANI T O, REHMAN S, et al. Support vector machines for wind speed prediction[J]. Renewable Energy, 2004, 29(6): 939-947.
|
[21] |
刘刚,彭春华,相龙阳.采用改进型多目标粒子群算法的电力系统环境经济调度[J].电网技术,2011,35(7): 139-144. LIU Gang, PENG Chun-hua, XIANG Long-yang. Economic-environmental dispatch using improved multi-objective particle swarm optimization[J]. Power System Technology, 2011, 35(7): 139-144.
|
[22] |
RASHEDI E, NEZAMABADI-POUR H, SARYAZDI S. GSA: a gravitational search algorithm[J]. Information Science, 2009, 179(13): 2232-2248.
|
[23] |
李志刚,吴文传,张伯明,等.一种基于高斯罚函数的大规模无功优化离散变量处理方法[J].中国电机工程学报,2013,33(4):68-76. LI Zhi-gang, WU Wen-chuan, ZHANG Bo-ming, et al. A large-scale reactive power optimization method based on Gaussian penalty function with discrete control variables[J]. Proceedings of the CSEE, 2013, 33(4): 68-76.
|
[24] |
王贺,胡志坚,陈珍,等.基于集合经验模态分解和最小二乘向量机的短期风速组合预测[J].电工技术学报,2014,29(4):237-245. WANG He, HU Zhi-jian, CHEN Zhen, et al. A hybrid model for short-term wind speed forecasting based on ensemble empirical mode decomposition and least squares support vector machines[J]. Transactions of China Electrotechnical Society, 2014, 29(4): 237-245.
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