Feature extraction and evaluation method of power engineering data under the framework of fusion
A multi-task fusion data evaluation model based on Multi-layer Perceptron(MLP),Gated Re-current Unit(GRU),and Graph Convolutional Networks(GCN)is proposed to address the issues of low data processing efficiency and insufficient intelligence and informatization of existing power engineering eval-uation and verification methods.On the basis of pre-processing engineering data,this model utilizes MLP,GRU,and GCN to extract deep features from multivariate data.The tensor fusion method is introduced into the multi-task learning model with adaptive weights to achieve feature level fusion of data information,and then the evaluation results are obtained through full connection processing between the shared layer and the output layer.The experiment results show that the root mean square error of the evaluation results of the proposed model is 0.035,the average absolute error is 0.014,and the determination coefficient is 0.993,all of which are superior to existing feature fusion data processing methods.
万方数据
维普
