考虑时空耦合特性的非侵入式综合能源系统多能设备负荷辨识方法

Non-invasive Comprehensive Energy System Multi Energy Equipment Load Identification Method Considering Spatiotemporal Coupling Characteristics

为了提升非侵入式综合能源系统多能设备负荷辨识的准确性,在充分考虑多能负荷时空耦合特性的基础上,提出了一种基于改进滑动窗口双边累计和(cumlative sum,CUSUM)、图半监督学习(graph semi supervised learning,GBSSL)和改进胶囊网络(improve capsule network,ICapsNet)的非侵入式综合能源系统多能设备负荷辨识方法。首先,引入自适应噪声值选取方法对滑动窗口双边CUSUM算法进行改进,并利用改进后的算法进行事件检测,之后通过GBSSL标记未标记的样本;其次,在CapsNet的基础上,改进相似度和加权求和计算方法,利用残差块结构卷积网络替代原卷积模块,并将极化自注意块引入主胶囊模块,构建ICapsNet;最后,利用不同的非侵入负荷辨识方法对采集的10 150个综合能源负荷数据进行负荷辨识,验证所提方法的优越性。实验结果表明:所提方法相较于BI-GRU、Bagging EL和DNN等主流非侵入负荷辨识方法,P_(recision)、R_(ecall)、F_(macro)和BA指标分别平均提高了1.77%、2.14%、1.94%和1.26%。由此可知所提方法对能够精准地辨识非侵入式综合能源系统多能设备负荷,且具有良好的运算效率和通用性。

To enhance the accuracy of load identification for multi-energy devices in non-invasive integrated energy systems,a method based on an improved sliding window bilateral CUSUM(cumulative sum),GBSSL(graph semi-supervised learning),and ICapsNet(improved capsule network) was proposed,considering the spatiotemporal coupling characteristics of multi-energy loads.Initially,an adaptive noise value selection method was introduced to enhance the sliding window bilateral CUSUM algorithm,and the improved algorithm was used for event detection,followed by labeling unlabeled samples through GBSSL.Subsequently,on the basis of CapsNet,the similarity and weighted sum calculation methods were improved,the residual block structure convolutional network replaces the original convolutional module,and a polarization self-attention block was introduced into the main capsule module to construct ICapsNet.Finally,different non-invasive load identification methods are used to identify the loads of 10 150 integrated energy load data collected,verifying the superiority of the proposed method.Experimental results show that compared to mainstream non-invasive load identification methods such as BI-GRU,Bagging EL,and DNN,the P_(recision),R_(ecall),F_(macro),and BA metrics are improved by an average of 1.77%,2.14%,1.94%,and 1.26%,respectively.The results indicates that the proposed method can accurately identify the loads of multi-energy devices in non-invasive integrated energy systems,with good computational efficiency and versatility.