提出了一种基于三维卷积和卷积长短期记忆(convolutional long short-term memory,CLSTM)神经网络的水产养殖溶解氧预测模型。首先,将输入向量及其转置相乘形成一个单通道矩阵,把一定时间段内的单通道矩阵堆叠成一个立方体作为输入数据...提出了一种基于三维卷积和卷积长短期记忆(convolutional long short-term memory,CLSTM)神经网络的水产养殖溶解氧预测模型。首先,将输入向量及其转置相乘形成一个单通道矩阵,把一定时间段内的单通道矩阵堆叠成一个立方体作为输入数据;然后,将输入数据进行连续两次三维卷积来细化溶解氧相关因素的特征,并删除池化层以简化计算;最后,将三维卷积抽取的特征结果输入CLSTM模型以提取时间维度的信息,在全连接层根据梯度下降算法将数据反向更新。采集湖北省襄阳市某家特种水产养殖有限公司的实际数据进行实验。结果表明:相比于传统BP神经网络模型、Conv3D、Conv2D,所提出的模型具有更快的训练收敛速度、更高的预测精度和更好的预测稳定性,可以满足实际生产的需要。展开更多
Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devi...Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.展开更多
针对现有能耗模型对动态工作负载波动具有低敏感性和低精度的问题,该文基于卷积长短期记忆(convolutional long short-term memory, ConvLSTM)神经网络,提出了用于移动边缘计算的服务器智能能耗模型(intelligence server energy consump...针对现有能耗模型对动态工作负载波动具有低敏感性和低精度的问题,该文基于卷积长短期记忆(convolutional long short-term memory, ConvLSTM)神经网络,提出了用于移动边缘计算的服务器智能能耗模型(intelligence server energy consumption model,IECM),用于预测和优化服务器的能量消耗。通过收集服务器运行时间参数,使用熵值法筛选和保留显著影响服务器能耗的参数。基于选定的参数,利用ConvLSTM神经网络训练服务器能耗模型的深度网络。与现有的能耗模型相比,IECM在CPU密集型、I/O密集型、内存密集型和混合型任务上,能够适应服务器工作负载的动态变化,并在能耗预测上具有更好的准确性。展开更多
文摘提出了一种基于三维卷积和卷积长短期记忆(convolutional long short-term memory,CLSTM)神经网络的水产养殖溶解氧预测模型。首先,将输入向量及其转置相乘形成一个单通道矩阵,把一定时间段内的单通道矩阵堆叠成一个立方体作为输入数据;然后,将输入数据进行连续两次三维卷积来细化溶解氧相关因素的特征,并删除池化层以简化计算;最后,将三维卷积抽取的特征结果输入CLSTM模型以提取时间维度的信息,在全连接层根据梯度下降算法将数据反向更新。采集湖北省襄阳市某家特种水产养殖有限公司的实际数据进行实验。结果表明:相比于传统BP神经网络模型、Conv3D、Conv2D,所提出的模型具有更快的训练收敛速度、更高的预测精度和更好的预测稳定性,可以满足实际生产的需要。
文摘Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.
文摘针对现有能耗模型对动态工作负载波动具有低敏感性和低精度的问题,该文基于卷积长短期记忆(convolutional long short-term memory, ConvLSTM)神经网络,提出了用于移动边缘计算的服务器智能能耗模型(intelligence server energy consumption model,IECM),用于预测和优化服务器的能量消耗。通过收集服务器运行时间参数,使用熵值法筛选和保留显著影响服务器能耗的参数。基于选定的参数,利用ConvLSTM神经网络训练服务器能耗模型的深度网络。与现有的能耗模型相比,IECM在CPU密集型、I/O密集型、内存密集型和混合型任务上,能够适应服务器工作负载的动态变化,并在能耗预测上具有更好的准确性。
