A Multiple Intertwined Deep Learning Model for Power Grid Data Verification

In recent years, the rapid development of artificial intelligence technology represented by deep learning has profoundly changed our way of life. Among them, the use of deep learning technology to check power grid data has also attracted more and more scholars' attention. In this paper, a deep learning model based on multiple intertwined networks is proposed for power grid data verification. Firstly, multiple groups of power grid data at different times are obtained, made into historical data sets and predicted data sets, and mapped into fixed length vectors to obtain the embedded matrix corresponding to the historical data and predicted data. Then, multiple intertwined networks is used to extract the features of the embedded matrix corresponding to the historical data and the predicted data, and the vector representation corresponding to the historical data and the predicted data is obtained. Finally, the cosine similarity is used to calculate the matching score between the historical data and the predicted data, so as to realize the data verification of the power grid. The experimental results show that the proposed deep learning model based on multiple intertwined networks is effective for power grid data verification.