基于不同优化准则和广义回归神经网络的风电功率非线性组合预测
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摘要
为提高风电功率预测精度,提出一种基于不同优化准则和广义回归神经网络(GRNN)的风电功率非线性组合预测方法。首先,基于灰色关联度理论,筛选出综合灰色关联度大于0的单项预测模型。然后,利用筛选出的单项预测模型以平均绝对误差最小、平均相对误差最小和均方根误差最小为优化准则构建线性组合优化模型。最后,利用GRNN神经网络对基于不同优化准则的线性组合模型进行非线性组合,得到优化模型。以实测风电功率数据对所提方法进行验证,仿真结果表明:与各单项预测模型、线性组合模型相比,所提优化模型的整体预测精度高,证明了该方法的有效性和实用性。
In order to improve the accuracy of wind power forecasting, a nonlinear combined model for wind power forecasting based on different optimization criteria and generalized regression neural network(GRNN) is proposed. The grey correlation analysis method is applied to select the forecasting models in which the comprehensive grey correlation values are greater than zero. Based on the three selected models, different linear combined forecasting models are built with the minimum average relative error, minimum mean absolute error, and minimum root mean square error as the optimization criterion, respectively. The GRNN is used to establish the nonlinear combined forecasting model by the three linear combined models and then obtain the optimized model. The verification with the real measured data of a wind farm shows that, compared with the single forecasting models, linear combined forecasting models, the proposed optimized model improves the forecasting accuracy effectively, which proves its effectiveness and practicability.
In order to improve the accuracy of wind power forecasting, a nonlinear combined model for wind power forecasting based on different optimization criteria and generalized regression neural network(GRNN) is proposed. The grey correlation analysis method is applied to select the forecasting models in which the comprehensive grey correlation values are greater than zero. Based on the three selected models, different linear combined forecasting models are built with the minimum average relative error, minimum mean absolute error, and minimum root mean square error as the optimization criterion, respectively. The GRNN is used to establish the nonlinear combined forecasting model by the three linear combined models and then obtain the optimized model. The verification with the real measured data of a wind farm shows that, compared with the single forecasting models, linear combined forecasting models, the proposed optimized model improves the forecasting accuracy effectively, which proves its effectiveness and practicability.
引文
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[15]王昕,黄柯,郑益慧,等.基于萤火虫算法-广义回归神经网络的光伏发电功率组合预测[J].电网技术,2017,41(2):455-461.WANG Xin,HUANG Ke,ZHENG Yihui,et al.Combined PV power forecast based on firefly algorithm generalized regression neural network[J].Power System Technology,2017,41(2):455-461.
[2]LUJANO-ROJAS J M,OSóRIO G J,MATIAS J C O,et al.A heuristic methodology to economic dispatch problem incorporating renewable power forecasting error and system reliability[J].Renewable Energy,2016,87(3):731-743.
[3]王勃,刘纯,张俊,等.基于Monte-Carlo方法的风电功率预测不确定性估计[J].高电压技术,2015,41(10):3385-3391.WANG Bo,LIU Chun,ZHANG Jun,et al.Uncertainty evaluation of wind power prediction based on monte-carlo method[J].High Voltage Engineering,2015,41(10):3385-3391.
[4]潘迪夫,刘辉,李燕飞.基于时间序列分析和卡尔曼滤波算法的风电场风速预测优化模型[J].电网技术,2008,32(7):82-86.PAN Difu,LIU Hui,LI Yanfei.A wind speed forecasting optimization model for wind farms based on time series analysis and Kalman filter algorithm[J].Power System Technology,2008,32(7):82-86.
[5]El-FOULY T H M,El-SAADANY E F,SALAMA M M A.Improved grey prediction rolling models for wind power prediction[J].IEEEProceedings on Generaton,Transmission and Distribution,2007,1(6):728-734.
[6]WANG J,HU J,MA K,et al.A self-adaptive hybrid approach for wind speed forecasting[J].Renewable Energy,2015,78(6):374-385.
[7]陈妮亚.短期风电功率预测方法研究[D].北京:北京航空航天大学,2014.CHEN Niya.Research on methodology of short-term wind power[D].Beijing,China:Beihang University,2014.
[8]程启明,张强,程尹曼,等.基于密度峰值层次聚类的短期光伏功率预测模型[J].高电压技术,2017,43(4):1214-1222.CHENG Qiming,ZHANG Qiang,CHENG Yinman,et al.Short-term photovoltaic power prediction model based on hierarchical clustering of density peaks algorithm[J].High Voltage Engineering,2017,43(4):1214-1222.
[9]苏学能,刘天琪,曹鸿谦,等.基于Hadoop架构的多重分布式BP神经网络的短期负荷预测方法[J].中国电机工程学报,2017,37(17):4966-4974.SU Xueneng,LIU Tianqi,CAO Hongqian,et al.A multiple distributed BP neural networks approach for short-term load forecasting based on Hadoop framework[J].Proceedings of the CSEE,2017,37(17):4966-4974.
[10]李冬辉,闫振林,姚乐乐,等.基于改进流形正则化极限学习机的短期电力股和预测[J].高电压技术,2016,42(7):2091-2099.LI Donghui,YAN Zhenlin,YAO Lele,et al.Short-term load forecasting based on improved manifold regularization extreme learning machine[J].High Voltage Engineering,2016,42(7):2091-2099.
[11]张露,卢继平,梅亦蕾,等.基于不同优化准则的风电功率预测[J].电力自动化设备,2015,35(5):139-145.ZHANG Lu,LU Jiping,MEI Yilei,et al.Wind power forecasting based on different optimization criterions[J].Electric Power Automation Equipment,2015,35(5):139-145.
[12]张国强,张伯明.基于组合预测的风电场风速及风电机功率预测[J].电力系统自动化,2009,33(18):92-95.ZHANG Guoqiang,ZHANG Boming.Wind speed and wind turbine output forecast based on combination method[J].Automation of Electric Power Systems,2009,33(18):92-95.
[13]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 Jiping,QIN Qiaoyun,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.
[14]王韶,杨江平,李逢兵,等.基于经验模式分解和神经网络的短期风速组合预测[J].电力系统保护与控制,2012,40(10):6-11.WANG Shao,YANG Jiangping,LI Fengbing,et al.Short-term wind speed forcasting based on EMD and ANN[J].Power System Protection and Control,2012,40(10):6-11.
[15]王昕,黄柯,郑益慧,等.基于萤火虫算法-广义回归神经网络的光伏发电功率组合预测[J].电网技术,2017,41(2):455-461.WANG Xin,HUANG Ke,ZHENG Yihui,et al.Combined PV power forecast based on firefly algorithm generalized regression neural network[J].Power System Technology,2017,41(2):455-461.