A Prediction Approach of Hot Electric Power Researches for Heterogeneous Time Spans

Abstract

Recently with the rapid growth of electric power literatures, it is hard to artificially track and process hot electric scientific researches. In the past most, professionals use simple statistics to get high-frequency words, which is time-consuming and ignores the similarity between words. Moreover, different researchers have different requirements for prediction time span. In the paper we propose a prediction system for hot electric scientific researches and gives its implementation. It is based on our previous work and we improve it to suit indefinite prediction period. The proposed embedded RNN prediction model is flexible for heterogeneous time spans and can return prediction results rapidly and accurately. Our extensive experiments demonstrated that our approach has acceptable precision ratio as well as training time in comparison to SVM, RNN and linear regression algorithms. It also performs better when the embedded layers are multiple.