针对电力负荷在线预测问题,结合多变量相空间重构以及多核函数LS-SVM(Least Squares Support Vector Machine),提出一种基于滑动窗口策略与改进人工鱼群算法(Artificial Fish Swarm Algorithm)的短期电力负荷在线预测综合优化方法。利...针对电力负荷在线预测问题,结合多变量相空间重构以及多核函数LS-SVM(Least Squares Support Vector Machine),提出一种基于滑动窗口策略与改进人工鱼群算法(Artificial Fish Swarm Algorithm)的短期电力负荷在线预测综合优化方法。利用多变量相空间重构还原真实电力系统动力学特性;将核函数进行排列组合,从而将组合核函数的构造问题转换为权值系数的优化问题,进一步将延迟时间、嵌入维数、LS-SVM参数以及核函数权值作为整体参数向量,利用混沌自适应人工鱼群算法对训练数据预测精度的适应度函数进行优化,从而得到最优的预测模型参数,最后通过滑动时窗策略将得到的预测模型对短期电力负荷进行在线预测,结果证明了提出方法的有效性。展开更多
针对短期电力负荷在线预测问题,结合多变量相空间重构以及多核函数最小二乘支持向量机(least squares support vector machine,LS-SVM),提出一种基于滑动窗口策略与改进人工鱼群算法(artificial fish swarm algorithm,AFSA)的短期电力...针对短期电力负荷在线预测问题,结合多变量相空间重构以及多核函数最小二乘支持向量机(least squares support vector machine,LS-SVM),提出一种基于滑动窗口策略与改进人工鱼群算法(artificial fish swarm algorithm,AFSA)的短期电力负荷在线预测综合优化方法。首先,利用多变量相空间重构还原真实电力系统动力学特性;然后,将核函数进行排列组合,从而将组合核函数的构造问题转换为权值系数的优化问题;进一步,将延迟时间、嵌入维数、LS-SVM参数及核函数权值作为整体参数向量,利用混沌自适应人工鱼群算法对训练数据预测精度的适应度函数进行优化,从而得到最优的预测模型参数;最后,通过滑动时窗策略将得到的预测模型对短期电力负荷进行在线预测,结果证明了提出方法的有效性。展开更多
大规模风电并网降低了电力系统惯量水平,增加了其暂态频率偏差的越限风险,然而现有频率预测模型对含风电系统的在线预测速度和精度都还不够,故需进一步优化频率偏差极值预测方法,用以系统的频率稳定评估。文章基于广域量测技术(wide are...大规模风电并网降低了电力系统惯量水平,增加了其暂态频率偏差的越限风险,然而现有频率预测模型对含风电系统的在线预测速度和精度都还不够,故需进一步优化频率偏差极值预测方法,用以系统的频率稳定评估。文章基于广域量测技术(wide area measurement system,WAMS),考虑风电并网对频率响应过程的影响,提出了一种物理-数据融合频率偏差极值在线预测方法。首先,利用广域量测信息对有功-频率开环解耦模型进行数据粘合,在此基础上形成可快速求解的物理-数据融合的暂态频率分析模型;其次,基于该模型,获得实时更新的频率偏差极值预测值,并提出“预测值误差指数”指标来量化预测精度,指导在线模型的自适应动态结果输出;最后,通过算例验证了所提频率偏差极值在线预测方法的快速性和准确性。展开更多
A nozzle clogging online forecasting model based on hydrodynamics engineering was developed, in which the actual flow rate was calculated from the mold width, thickness, and casting speed. There is a linear relationsh...A nozzle clogging online forecasting model based on hydrodynamics engineering was developed, in which the actual flow rate was calculated from the mold width, thickness, and casting speed. There is a linear relationship between the theoretical flow rate and the slide gate opening ratio as the molten steel level, argon flow rate, and the top slag weight are kept constant, and the relationship can be obtained by regression of the data collected at the beginning of the first heat in each casting sequence when the nozzle clogging does not occur. Then, during the casting, the theoretical flow rate can be calculated at intervals of one second. Comparing the theoretical flow rate with the actual flow rate, the online nozzle clogging ratio can be obtained at intervals of one second. The computer model based on the conception of the nozzle clogging ratio can display the degree of the nozzle clogging intuitively.展开更多
Considering that the prediction accuracy of the traditional traffic flow forecasting model is low,based on kernel adaptive filter(KAF)algorithm,kernel least mean square(KLMS)algorithm and fixed-budget kernel recursive...Considering that the prediction accuracy of the traditional traffic flow forecasting model is low,based on kernel adaptive filter(KAF)algorithm,kernel least mean square(KLMS)algorithm and fixed-budget kernel recursive least-square(FB-KRLS)algorithm are presented for online adaptive prediction.The computational complexity of the KLMS algorithm is low and does not require additional solution paradigm constraints,but its regularization process can solve the problem of regularization performance degradation in high-dimensional data processing.To reduce the computational complexity,the sparse criterion is introduced into the KLMS algorithm.To further improve forecasting accuracy,FB-KRLS algorithm is proposed.It is an online learning method with fixed memory budget,and it is capable of recursively learning a nonlinear mapping and changing over time.In contrast to a previous approximate linear dependence(ALD)based technique,the purpose of the presented algorithm is not to prune the oldest data point in every time instant but it aims to prune the least significant data point,thus suppressing the growth of kernel matrix.In order to verify the validity of the proposed methods,they are applied to one-step and multi-step predictions of traffic flow in Beijing.Under the same conditions,they are compared with online adaptive ALD-KRLS method and other kernel learning methods.Experimental results show that the proposed KAF algorithms can improve the prediction accuracy,and its online learning ability meets the actual requirements of traffic flow and contributes to real-time online forecasting of traffic flow.展开更多
Online sequential extreme learning machine(OS-ELM)for single-hidden layer feedforward networks(SLFNs)is an effective machine learning algorithm.But OS-ELM has some underlying weaknesses of neglecting time series timel...Online sequential extreme learning machine(OS-ELM)for single-hidden layer feedforward networks(SLFNs)is an effective machine learning algorithm.But OS-ELM has some underlying weaknesses of neglecting time series timeliness and being incapable to provide quantitative uncertainty for prediction.To overcome these shortcomings,a time series prediction method is presented based on the combination of OS-ELM with adaptive forgetting factor(AFF-OS-ELM)and bootstrap(B-AFF-OS-ELM).Firstly,adaptive forgetting factor is added into OS-ELM for adjusting the effective window length of training data during OS-ELM sequential learning phase.Secondly,the current bootstrap is developed to fit time series prediction online.Then associated with improved bootstrap,the proposed method can compute prediction interval as uncertainty information,meanwhile the improved bootstrap enhances prediction accuracy and stability of AFF-OS-ELM.Performances of B-AFF-OS-ELM are benchmarked with other traditional and improved OS-ELM on simulation and practical time series data.Results indicate the significant performances achieved by B-AFF-OS-ELM.展开更多
文摘针对电力负荷在线预测问题,结合多变量相空间重构以及多核函数LS-SVM(Least Squares Support Vector Machine),提出一种基于滑动窗口策略与改进人工鱼群算法(Artificial Fish Swarm Algorithm)的短期电力负荷在线预测综合优化方法。利用多变量相空间重构还原真实电力系统动力学特性;将核函数进行排列组合,从而将组合核函数的构造问题转换为权值系数的优化问题,进一步将延迟时间、嵌入维数、LS-SVM参数以及核函数权值作为整体参数向量,利用混沌自适应人工鱼群算法对训练数据预测精度的适应度函数进行优化,从而得到最优的预测模型参数,最后通过滑动时窗策略将得到的预测模型对短期电力负荷进行在线预测,结果证明了提出方法的有效性。
文摘针对短期电力负荷在线预测问题,结合多变量相空间重构以及多核函数最小二乘支持向量机(least squares support vector machine,LS-SVM),提出一种基于滑动窗口策略与改进人工鱼群算法(artificial fish swarm algorithm,AFSA)的短期电力负荷在线预测综合优化方法。首先,利用多变量相空间重构还原真实电力系统动力学特性;然后,将核函数进行排列组合,从而将组合核函数的构造问题转换为权值系数的优化问题;进一步,将延迟时间、嵌入维数、LS-SVM参数及核函数权值作为整体参数向量,利用混沌自适应人工鱼群算法对训练数据预测精度的适应度函数进行优化,从而得到最优的预测模型参数;最后,通过滑动时窗策略将得到的预测模型对短期电力负荷进行在线预测,结果证明了提出方法的有效性。
文摘大规模风电并网降低了电力系统惯量水平,增加了其暂态频率偏差的越限风险,然而现有频率预测模型对含风电系统的在线预测速度和精度都还不够,故需进一步优化频率偏差极值预测方法,用以系统的频率稳定评估。文章基于广域量测技术(wide area measurement system,WAMS),考虑风电并网对频率响应过程的影响,提出了一种物理-数据融合频率偏差极值在线预测方法。首先,利用广域量测信息对有功-频率开环解耦模型进行数据粘合,在此基础上形成可快速求解的物理-数据融合的暂态频率分析模型;其次,基于该模型,获得实时更新的频率偏差极值预测值,并提出“预测值误差指数”指标来量化预测精度,指导在线模型的自适应动态结果输出;最后,通过算例验证了所提频率偏差极值在线预测方法的快速性和准确性。
基金financially supported by the State EconomicTrade Commission of China (No.OIBK-098-02-07)
文摘A nozzle clogging online forecasting model based on hydrodynamics engineering was developed, in which the actual flow rate was calculated from the mold width, thickness, and casting speed. There is a linear relationship between the theoretical flow rate and the slide gate opening ratio as the molten steel level, argon flow rate, and the top slag weight are kept constant, and the relationship can be obtained by regression of the data collected at the beginning of the first heat in each casting sequence when the nozzle clogging does not occur. Then, during the casting, the theoretical flow rate can be calculated at intervals of one second. Comparing the theoretical flow rate with the actual flow rate, the online nozzle clogging ratio can be obtained at intervals of one second. The computer model based on the conception of the nozzle clogging ratio can display the degree of the nozzle clogging intuitively.
基金National Natural Science Foundation of China(No.51467008)
文摘Considering that the prediction accuracy of the traditional traffic flow forecasting model is low,based on kernel adaptive filter(KAF)algorithm,kernel least mean square(KLMS)algorithm and fixed-budget kernel recursive least-square(FB-KRLS)algorithm are presented for online adaptive prediction.The computational complexity of the KLMS algorithm is low and does not require additional solution paradigm constraints,but its regularization process can solve the problem of regularization performance degradation in high-dimensional data processing.To reduce the computational complexity,the sparse criterion is introduced into the KLMS algorithm.To further improve forecasting accuracy,FB-KRLS algorithm is proposed.It is an online learning method with fixed memory budget,and it is capable of recursively learning a nonlinear mapping and changing over time.In contrast to a previous approximate linear dependence(ALD)based technique,the purpose of the presented algorithm is not to prune the oldest data point in every time instant but it aims to prune the least significant data point,thus suppressing the growth of kernel matrix.In order to verify the validity of the proposed methods,they are applied to one-step and multi-step predictions of traffic flow in Beijing.Under the same conditions,they are compared with online adaptive ALD-KRLS method and other kernel learning methods.Experimental results show that the proposed KAF algorithms can improve the prediction accuracy,and its online learning ability meets the actual requirements of traffic flow and contributes to real-time online forecasting of traffic flow.
文摘Online sequential extreme learning machine(OS-ELM)for single-hidden layer feedforward networks(SLFNs)is an effective machine learning algorithm.But OS-ELM has some underlying weaknesses of neglecting time series timeliness and being incapable to provide quantitative uncertainty for prediction.To overcome these shortcomings,a time series prediction method is presented based on the combination of OS-ELM with adaptive forgetting factor(AFF-OS-ELM)and bootstrap(B-AFF-OS-ELM).Firstly,adaptive forgetting factor is added into OS-ELM for adjusting the effective window length of training data during OS-ELM sequential learning phase.Secondly,the current bootstrap is developed to fit time series prediction online.Then associated with improved bootstrap,the proposed method can compute prediction interval as uncertainty information,meanwhile the improved bootstrap enhances prediction accuracy and stability of AFF-OS-ELM.Performances of B-AFF-OS-ELM are benchmarked with other traditional and improved OS-ELM on simulation and practical time series data.Results indicate the significant performances achieved by B-AFF-OS-ELM.
