为准确预测电力市场中的短期电价,将最大信息系数(maximal information coefficient,MIC)相关性分析与改进多层级门控长短期记忆网络(multi-hierachy gated long shortterm memory,MHG-LSTM)相结合,提出一种新型短期电价预测方法。该方...为准确预测电力市场中的短期电价,将最大信息系数(maximal information coefficient,MIC)相关性分析与改进多层级门控长短期记忆网络(multi-hierachy gated long shortterm memory,MHG-LSTM)相结合,提出一种新型短期电价预测方法。该方法首先对备选序列与预测电价序列做MIC相关性分析,在此基础上筛选备选序列并经小波变换合成神经网络输入序列,有效增加了输入中与预测电价相关的信息密度;其次,对传统LSTM进行创新性改进,提出用两级遗忘门和输入门替换传统的一级门控机构的MHG-LSTM模型,提高了神经网络选择和提取高频电价序列特征的能力。在PJM市场日前电价数据集上对所提方法进行仿真实验,实验结果表明,该方法的预测误差仅为4.506%,相比已有预测方法有效提升了短期电价的预测精度,且具有很强的普适性,可应用于电力市场短期电价预测,为市场参与者和监管机构提供有力决策依据。展开更多
受配电物联网多业务场景全息感知信息中的冲击数据、异常数据、冲突数据等“脏数据”影响,海量数据延伸过程面临数据品质不高的问题,进而降低了数据的价值质量、规律挖掘和趋势预测的前景。为此,提出了一种基于留存分析方法结合长短期...受配电物联网多业务场景全息感知信息中的冲击数据、异常数据、冲突数据等“脏数据”影响,海量数据延伸过程面临数据品质不高的问题,进而降低了数据的价值质量、规律挖掘和趋势预测的前景。为此,提出了一种基于留存分析方法结合长短期记忆算法(Long Short Term Memory,LSTM)的数据分析模型,并以此对配网电力数据发展趋势进行预测。该模型采用留存分析方法对海量感知数据中的“脏数据”进行清洗,并在此基础上利用清洗后的数据共性特征对LSTM中的遗忘门进行改进和优化,实现了清洗后价值数据规律的挖掘和趋势的预测。以新能源汽车负荷预测为例,实验结果表明,相较于同类数据趋势预测的整合移动平均自回归算法(Auto Regressive Integrated Moving Average,ARIMA)与传统LSTM算法,该文方法整体预测准确率分别提升了16.26%、16.84%,而各时段预测值的均方根误差(RMSE)较ARIMA算法和LSTM算法则分别降低17%和22.7%,这表明该方法可有效处理配电物联网海量数据的优良率和预测精度,为后续决策提供数据依据。展开更多
[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been propo...[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.展开更多
文摘为准确预测电力市场中的短期电价,将最大信息系数(maximal information coefficient,MIC)相关性分析与改进多层级门控长短期记忆网络(multi-hierachy gated long shortterm memory,MHG-LSTM)相结合,提出一种新型短期电价预测方法。该方法首先对备选序列与预测电价序列做MIC相关性分析,在此基础上筛选备选序列并经小波变换合成神经网络输入序列,有效增加了输入中与预测电价相关的信息密度;其次,对传统LSTM进行创新性改进,提出用两级遗忘门和输入门替换传统的一级门控机构的MHG-LSTM模型,提高了神经网络选择和提取高频电价序列特征的能力。在PJM市场日前电价数据集上对所提方法进行仿真实验,实验结果表明,该方法的预测误差仅为4.506%,相比已有预测方法有效提升了短期电价的预测精度,且具有很强的普适性,可应用于电力市场短期电价预测,为市场参与者和监管机构提供有力决策依据。
文摘受配电物联网多业务场景全息感知信息中的冲击数据、异常数据、冲突数据等“脏数据”影响,海量数据延伸过程面临数据品质不高的问题,进而降低了数据的价值质量、规律挖掘和趋势预测的前景。为此,提出了一种基于留存分析方法结合长短期记忆算法(Long Short Term Memory,LSTM)的数据分析模型,并以此对配网电力数据发展趋势进行预测。该模型采用留存分析方法对海量感知数据中的“脏数据”进行清洗,并在此基础上利用清洗后的数据共性特征对LSTM中的遗忘门进行改进和优化,实现了清洗后价值数据规律的挖掘和趋势的预测。以新能源汽车负荷预测为例,实验结果表明,相较于同类数据趋势预测的整合移动平均自回归算法(Auto Regressive Integrated Moving Average,ARIMA)与传统LSTM算法,该文方法整体预测准确率分别提升了16.26%、16.84%,而各时段预测值的均方根误差(RMSE)较ARIMA算法和LSTM算法则分别降低17%和22.7%,这表明该方法可有效处理配电物联网海量数据的优良率和预测精度,为后续决策提供数据依据。
文摘[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.
