微服务的按需伸缩对提高集群的资源利用率至关重要,而按需伸缩的前提是集群能够对资源需求进行精准预测。当前基于规则响应式的资源管理策略仍是产业界的主流方式,学术界结合机器学习的资源负载预测方法仍存在预测不够精准等问题。因此...微服务的按需伸缩对提高集群的资源利用率至关重要,而按需伸缩的前提是集群能够对资源需求进行精准预测。当前基于规则响应式的资源管理策略仍是产业界的主流方式,学术界结合机器学习的资源负载预测方法仍存在预测不够精准等问题。因此,提出一种基于微服务依赖程度的负载预测模型。通过基于DTW(Dynamic Time Warping)改进的容器依赖程度检测算法,对容器进行依赖程度评估。分析存在强依赖关系的容器之间指标的相关性,选择相关性较高的指标作为模型的输入特征变量。预测模型采用Seq2Seq(Sequence to Sequence)编解码模型,并结合注意力机制和残差LSTM来提升模型预测的精准性和稳定性。实验表明,该模型预测效果显著,误差评价指标MAE、RMSE、MAPE相较于另外两个深度学习模型平均降低了48%、35%、51%,能够有效预测出存在强依赖关系容器的短时负载。展开更多
为解决以往船舶轨迹预测模型在训练过程中未能合理应对多步预测任务间关联性的问题,构建了一种融合频域分析方法和Seq2Seq模型的船舶轨迹预测算法(Time-Frequency based Seqence to Seqence Model,TF-Seq2Seq)。基于自动识别系统数据特...为解决以往船舶轨迹预测模型在训练过程中未能合理应对多步预测任务间关联性的问题,构建了一种融合频域分析方法和Seq2Seq模型的船舶轨迹预测算法(Time-Frequency based Seqence to Seqence Model,TF-Seq2Seq)。基于自动识别系统数据特点设计了系列预处理操作;用融合频域分析方法和Seq2Seq模型的改进算法捕获船舶轨迹序列的关联性。基于真实数据进行实例分析,结果表明基于长短时记忆单元的Seq2Seq改进算法多种指标上表现最优,均方误差较原算法降低了12.9%。算法改进能更好地发挥模型优势,提高船舶轨迹预测精度。展开更多
Wind speed is a crucial parameter affecting wind energy utilization.However,its volatility leads to time-varying power output.Herein,a novel Seq2Seq model integrating deep learning,data denoising,and a shape-aware los...Wind speed is a crucial parameter affecting wind energy utilization.However,its volatility leads to time-varying power output.Herein,a novel Seq2Seq model integrating deep learning,data denoising,and a shape-aware loss function is proposed for accurate multistep wind speed forecasting.In this model,the wind speed data is first denoised using the maximal overlap discrete wavelet transform.Next,an encoder-decoder network based on a temporal convolutional network,bidirectional gated recurrent unit,and multihead self-attention is employed for forecasting.Additionally,to enhance the ability of the model to identify temporal dynamics,a shape-aware loss function,ITILDE-Q,is employed in the model.To verify the effectiveness of the proposed model,a comparative experiment and an ablation experiment were conducted using three datasets of measured wind speeds.Three error metrics and a similarity metric were adopted for comprehensive evaluation.The experimental results showed that the proposed model consistently outperforms benchmark models in all tested forecasting scenarios,with particularly pronounced differences in performance over longer forecast horizons.Furthermore,the synergistic interaction of the three key components contributes to the extraordinary performance of the proposed model.展开更多
自动抄表(Automatic Meter Reading,AMR)在变电站电表读数中具有重要的应用价值。近年来,深度学习图像识别技术在AMR领域取得了显著进展。然而,现有方法大多依赖于计数器检测、分割和识别的3阶段流程,存在复杂性和效率方面的问题。为提...自动抄表(Automatic Meter Reading,AMR)在变电站电表读数中具有重要的应用价值。近年来,深度学习图像识别技术在AMR领域取得了显著进展。然而,现有方法大多依赖于计数器检测、分割和识别的3阶段流程,存在复杂性和效率方面的问题。为提升AMR的准确性与效率,首次将序列到序列(Sequence-to-Sequence,Seq2Seq)架构引入该任务,结合YOLOv5进行计数器检测,并利用Seq2Seq架构直接识别计数器,省略了传统流程中的计数器分割步骤。此外,还提出改进注意力机制的Seq2Seq架构,以优化信息传递与特征对齐。在UFPR-AMR公开数据集上的实验表明,改进方法的准确率达到了92.5%,比原方法提升了1.25%,这一结果验证了所提出的方法在AMR任务中的有效性。展开更多
文摘微服务的按需伸缩对提高集群的资源利用率至关重要,而按需伸缩的前提是集群能够对资源需求进行精准预测。当前基于规则响应式的资源管理策略仍是产业界的主流方式,学术界结合机器学习的资源负载预测方法仍存在预测不够精准等问题。因此,提出一种基于微服务依赖程度的负载预测模型。通过基于DTW(Dynamic Time Warping)改进的容器依赖程度检测算法,对容器进行依赖程度评估。分析存在强依赖关系的容器之间指标的相关性,选择相关性较高的指标作为模型的输入特征变量。预测模型采用Seq2Seq(Sequence to Sequence)编解码模型,并结合注意力机制和残差LSTM来提升模型预测的精准性和稳定性。实验表明,该模型预测效果显著,误差评价指标MAE、RMSE、MAPE相较于另外两个深度学习模型平均降低了48%、35%、51%,能够有效预测出存在强依赖关系容器的短时负载。
文摘为解决以往船舶轨迹预测模型在训练过程中未能合理应对多步预测任务间关联性的问题,构建了一种融合频域分析方法和Seq2Seq模型的船舶轨迹预测算法(Time-Frequency based Seqence to Seqence Model,TF-Seq2Seq)。基于自动识别系统数据特点设计了系列预处理操作;用融合频域分析方法和Seq2Seq模型的改进算法捕获船舶轨迹序列的关联性。基于真实数据进行实例分析,结果表明基于长短时记忆单元的Seq2Seq改进算法多种指标上表现最优,均方误差较原算法降低了12.9%。算法改进能更好地发挥模型优势,提高船舶轨迹预测精度。
基金supported by the National Natural Science Foundation of China(No.52171284)。
文摘Wind speed is a crucial parameter affecting wind energy utilization.However,its volatility leads to time-varying power output.Herein,a novel Seq2Seq model integrating deep learning,data denoising,and a shape-aware loss function is proposed for accurate multistep wind speed forecasting.In this model,the wind speed data is first denoised using the maximal overlap discrete wavelet transform.Next,an encoder-decoder network based on a temporal convolutional network,bidirectional gated recurrent unit,and multihead self-attention is employed for forecasting.Additionally,to enhance the ability of the model to identify temporal dynamics,a shape-aware loss function,ITILDE-Q,is employed in the model.To verify the effectiveness of the proposed model,a comparative experiment and an ablation experiment were conducted using three datasets of measured wind speeds.Three error metrics and a similarity metric were adopted for comprehensive evaluation.The experimental results showed that the proposed model consistently outperforms benchmark models in all tested forecasting scenarios,with particularly pronounced differences in performance over longer forecast horizons.Furthermore,the synergistic interaction of the three key components contributes to the extraordinary performance of the proposed model.
文摘针对新能源大规模并网带来的消纳问题,提出一种考虑源荷双侧弹性资源的日前调度方法.首先,对深度调峰机组、可平移负荷和可削减负荷的弹性调节能力进行分析,建立含弹性资源的电力系统调度模型;然后,提出一种基于Conv-Seq2Seq (convolutional sequence to sequence)模型的日前调度方法,使用多层卷积神经网络作为编码器对负荷预测数据等信息进行提取,改进深度学习网络信息提取的能力和速度,并使用门控循环单元作为解码器对编码器提取的信息进行解码,以输出调度计划;最后,通过辅助决策修正来确保调度计划的安全性.基于改进的IEEE39节点算例验证所提出方法的有效性和正确性.
文摘自动抄表(Automatic Meter Reading,AMR)在变电站电表读数中具有重要的应用价值。近年来,深度学习图像识别技术在AMR领域取得了显著进展。然而,现有方法大多依赖于计数器检测、分割和识别的3阶段流程,存在复杂性和效率方面的问题。为提升AMR的准确性与效率,首次将序列到序列(Sequence-to-Sequence,Seq2Seq)架构引入该任务,结合YOLOv5进行计数器检测,并利用Seq2Seq架构直接识别计数器,省略了传统流程中的计数器分割步骤。此外,还提出改进注意力机制的Seq2Seq架构,以优化信息传递与特征对齐。在UFPR-AMR公开数据集上的实验表明,改进方法的准确率达到了92.5%,比原方法提升了1.25%,这一结果验证了所提出的方法在AMR任务中的有效性。
