为更准确预测输电线路未来允许的输送容量,提出利用动态提高输电线路输送容量(dynamic line rating,DLR)系统及混沌理论预测线路的输送容量。利用改进的C-C算法可靠性高、计算速度快的特点对容量时间序列进行相空间重构,并证实了线路允...为更准确预测输电线路未来允许的输送容量,提出利用动态提高输电线路输送容量(dynamic line rating,DLR)系统及混沌理论预测线路的输送容量。利用改进的C-C算法可靠性高、计算速度快的特点对容量时间序列进行相空间重构,并证实了线路允许输送容量具有混沌特性,可运用混沌理论预测线路输送的容量。然后采用基于奇异值分解的混沌时间序列Volterra方法对一条安装有DLR系统的110kV线路可输送的容量及线路可能发生的热过载故障进行预测。预测结果显示该方法能够反映容量序列未来变化的趋势及线路发生热过载故障的风险性,提高了预测的精度,是有效、可行的。展开更多
Autoencoder-based rating prediction methods with external attributes have received wide attention due to their ability to accurately capture users'preferences.However,existing methods still have two significant li...Autoencoder-based rating prediction methods with external attributes have received wide attention due to their ability to accurately capture users'preferences.However,existing methods still have two significant limitations:i)External attributes are often unavailable in the real world due to privacy issues,leading to low quality of representations;and ii)existing methods lack considering complex associations in users'rating behaviors during the encoding process.To meet these challenges,this paper innovatively proposes an inherent-attribute-aware dual-graph autoencoder,named IADGAE,for rating prediction.To address the low quality of representations due to the unavailability of external attributes,we propose an inherent attribute perception module that mines inductive user active patterns and item popularity patterns from users'rating behaviors to strengthen user and item representations.To exploit the complex associations hidden in users’rating behaviors,we design an encoder on the item-item co-occurrence graph to capture the co-occurrence frequency features among items.Moreover,we propose a dual-graph feature encoder framework to simultaneously encode and fuse the high-order representations learned from the user-item rating graph and item-item co-occurrence graph.Extensive experiments on three real datasets demonstrate that IADGAE is effective and outperforms existing rating prediction methods,which achieves a significant improvement of 4.51%~41.63%in the RMSE metric.展开更多
文摘为更准确预测输电线路未来允许的输送容量,提出利用动态提高输电线路输送容量(dynamic line rating,DLR)系统及混沌理论预测线路的输送容量。利用改进的C-C算法可靠性高、计算速度快的特点对容量时间序列进行相空间重构,并证实了线路允许输送容量具有混沌特性,可运用混沌理论预测线路输送的容量。然后采用基于奇异值分解的混沌时间序列Volterra方法对一条安装有DLR系统的110kV线路可输送的容量及线路可能发生的热过载故障进行预测。预测结果显示该方法能够反映容量序列未来变化的趋势及线路发生热过载故障的风险性,提高了预测的精度,是有效、可行的。
基金supported in part by National Natural Science Foundation of China(U21B2015,61972300)in part by Young Scientists Fund of the National Natural Science Foundation of China(62202356)+1 种基金in part by Young Talent Fund of Association for Science and Technology in Shaanxi(20220113)in part by Intelligent Financial Software Engineering New Technology Joint Laboratory Project(99901220858)。
文摘Autoencoder-based rating prediction methods with external attributes have received wide attention due to their ability to accurately capture users'preferences.However,existing methods still have two significant limitations:i)External attributes are often unavailable in the real world due to privacy issues,leading to low quality of representations;and ii)existing methods lack considering complex associations in users'rating behaviors during the encoding process.To meet these challenges,this paper innovatively proposes an inherent-attribute-aware dual-graph autoencoder,named IADGAE,for rating prediction.To address the low quality of representations due to the unavailability of external attributes,we propose an inherent attribute perception module that mines inductive user active patterns and item popularity patterns from users'rating behaviors to strengthen user and item representations.To exploit the complex associations hidden in users’rating behaviors,we design an encoder on the item-item co-occurrence graph to capture the co-occurrence frequency features among items.Moreover,we propose a dual-graph feature encoder framework to simultaneously encode and fuse the high-order representations learned from the user-item rating graph and item-item co-occurrence graph.Extensive experiments on three real datasets demonstrate that IADGAE is effective and outperforms existing rating prediction methods,which achieves a significant improvement of 4.51%~41.63%in the RMSE metric.
