A short-term load forecasting of BiLSTM based on grey relational analysis and attention model

Abstract

In this paper, a short-term load forecasting of bi-directional long short-term memory (BiLSTM) neural network based on grey relational analysis (GRA) and attention model (AM) is proposed. Firstly, the GRA is used to analyze the correlation between the load and weather factors, and the optimal feature set affecting load is extracted and selected as the input of the prediction model. Then, the AM is used to tune the BiLSTM neural network model parameters. Finally, the BiLSTM neural network model is used for load prediction and being verified with sample data. Compared with other prediction models, the model proposed in this paper shows higher prediction accuracy.

Bearing temperature prediction of hydroelectric unit based on PSO-SVR

“…Grey correlation analysis measures the actual correlation between target parameters and variable parameters based on the degree of similarity or dissimilarity among relevant factors. This approach allows for the extraction of the required variable parameters and facilitates the organization and processing of correlation information [2]. The analysis results are presented in Table 1.…”