风电功率预测对电力系统的安全稳定运行具有重要意义。针对多风电场的超短期概率预测问题,提出了一种基于Bagging混合策略和核密度估计(kernel density estimation,KDE)的稀疏向量自回归预测方法。首先通过时间序列分解和余项自举,生成...风电功率预测对电力系统的安全稳定运行具有重要意义。针对多风电场的超短期概率预测问题,提出了一种基于Bagging混合策略和核密度估计(kernel density estimation,KDE)的稀疏向量自回归预测方法。首先通过时间序列分解和余项自举,生成若干自举时间序列。对于每个时间序列,采用向量自回归(vector autoregression,VAR)模型进行预测。针对传统模型在风场数量较多时容易出现的过拟合问题,采用稀疏向量自回归模型,筛选最有效的回归系数,得到稀疏系数矩阵。每个时间序列训练的预测模型分别产生点预测结果,对于多重点预测结果,使用KDE方法产生概率密度的预测结果。在真实风电集群数据上,验证所提多场站概率预测方法的有效性,采用分位数得分评估概率预测精度。相关实验结果表明,该方法可以有效提高概率预测精度。展开更多
This study considered and predicted blast-induced ground vibration(PPV)in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms.Accordingly,four machine lear...This study considered and predicted blast-induced ground vibration(PPV)in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms.Accordingly,four machine learning algorithms,including support vector regression(SVR),extra trees(ExTree),K-nearest neighbors(KNN),and decision tree regression(DTR),were used as the base models for the purposes of combination and PPV initial prediction.The bagging regressor(BA)was then applied to combine these base models with the efforts of variance reduction,overfitting elimination,and generating more robust predictive models,abbreviated as BA-ExTree,BAKNN,BA-SVR,and BA-DTR.It is emphasized that the ExTree model has not been considered for predicting blastinduced ground vibration before,and the bagging of ExTree is an innovation aiming to improve the accuracy of the inherently ExTree model,as well.In addition,two empirical models(i.e.,USBM and Ambraseys)were also treated and compared with the bagging models to gain a comprehensive assessment.With this aim,we collected 300 blasting events with different parameters at the Sin Quyen copper mine(Vietnam),and the produced PPV values were also measured.They were then compiled as the dataset to develop the PPV predictive models.The results revealed that the bagging models provided better performance than the empirical models,except for the BA-DTR model.Of those,the BA-ExTree is the best model with the highest accuracy(i.e.,88.8%).Whereas,the empirical models only provided the accuracy from 73.6%–76%.The details of comparisons and assessments were also presented in this study.展开更多
Skyrmion bags are spin structures with arbitrary topological charges, each of which is composed of a big skyrmion and several small skyrmions. In this work, by using an in-plane alternating current(AC) magnetic field,...Skyrmion bags are spin structures with arbitrary topological charges, each of which is composed of a big skyrmion and several small skyrmions. In this work, by using an in-plane alternating current(AC) magnetic field, we investigate the spinwave modes of skyrmion bags, which behave differently from the clockwise(CW) rotation mode and the counterclockwise(CCW) rotation mode of skyrmions because of their complex spin topological structures. The in-plane excitation power spectral density shows that each skyrmion bag possesses four resonance frequencies. By further studying the spin dynamics of a skyrmion bag at each resonance frequency, the four spin-wave modes, i.e., a CCW-CW mode, two CW-breathing modes with different resonance strengths, and an inner CCW mode, appear as a composition mode of outer skyrmion–inner skyrmions. Our results are helpful in understanding the in-plane spin excitation of skyrmion bags, which may contribute to the characterization and detection of skyrmion bags, as well as the applications in logic devices.展开更多
文摘风电功率预测对电力系统的安全稳定运行具有重要意义。针对多风电场的超短期概率预测问题,提出了一种基于Bagging混合策略和核密度估计(kernel density estimation,KDE)的稀疏向量自回归预测方法。首先通过时间序列分解和余项自举,生成若干自举时间序列。对于每个时间序列,采用向量自回归(vector autoregression,VAR)模型进行预测。针对传统模型在风场数量较多时容易出现的过拟合问题,采用稀疏向量自回归模型,筛选最有效的回归系数,得到稀疏系数矩阵。每个时间序列训练的预测模型分别产生点预测结果,对于多重点预测结果,使用KDE方法产生概率密度的预测结果。在真实风电集群数据上,验证所提多场站概率预测方法的有效性,采用分位数得分评估概率预测精度。相关实验结果表明,该方法可以有效提高概率预测精度。
基金funded by Vietnam National Foundation for Science and Tech-nology Development(NAFOSTED)under Grant No.105.99-2019.309.
文摘This study considered and predicted blast-induced ground vibration(PPV)in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms.Accordingly,four machine learning algorithms,including support vector regression(SVR),extra trees(ExTree),K-nearest neighbors(KNN),and decision tree regression(DTR),were used as the base models for the purposes of combination and PPV initial prediction.The bagging regressor(BA)was then applied to combine these base models with the efforts of variance reduction,overfitting elimination,and generating more robust predictive models,abbreviated as BA-ExTree,BAKNN,BA-SVR,and BA-DTR.It is emphasized that the ExTree model has not been considered for predicting blastinduced ground vibration before,and the bagging of ExTree is an innovation aiming to improve the accuracy of the inherently ExTree model,as well.In addition,two empirical models(i.e.,USBM and Ambraseys)were also treated and compared with the bagging models to gain a comprehensive assessment.With this aim,we collected 300 blasting events with different parameters at the Sin Quyen copper mine(Vietnam),and the produced PPV values were also measured.They were then compiled as the dataset to develop the PPV predictive models.The results revealed that the bagging models provided better performance than the empirical models,except for the BA-DTR model.Of those,the BA-ExTree is the best model with the highest accuracy(i.e.,88.8%).Whereas,the empirical models only provided the accuracy from 73.6%–76%.The details of comparisons and assessments were also presented in this study.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 12104124 and 12274111)the Natural Science Foundation of Hebei Province, China (Grant Nos. A2021201001 and A2021201008)+4 种基金the Central Guidance Fund on the Local Science and Technology Development of Hebei Province, China (Grant No. 236Z0601G)the Post-graduate’s Innovation Fund Project of Hebei Province, China (Grant No. CXZZSS2023007)the Advanced Talents Incubation Program of the Hebei University, China (Grant Nos. 521000981395, 521000981423, 521000981394, and 521000981390)the Research Foundation of Chongqing University of Science and technology, China (Grant No. ckrc2019017)the High-Performance Computing Center of Hebei University, China。
文摘Skyrmion bags are spin structures with arbitrary topological charges, each of which is composed of a big skyrmion and several small skyrmions. In this work, by using an in-plane alternating current(AC) magnetic field, we investigate the spinwave modes of skyrmion bags, which behave differently from the clockwise(CW) rotation mode and the counterclockwise(CCW) rotation mode of skyrmions because of their complex spin topological structures. The in-plane excitation power spectral density shows that each skyrmion bag possesses four resonance frequencies. By further studying the spin dynamics of a skyrmion bag at each resonance frequency, the four spin-wave modes, i.e., a CCW-CW mode, two CW-breathing modes with different resonance strengths, and an inner CCW mode, appear as a composition mode of outer skyrmion–inner skyrmions. Our results are helpful in understanding the in-plane spin excitation of skyrmion bags, which may contribute to the characterization and detection of skyrmion bags, as well as the applications in logic devices.
