Short-term Load Forecast Based on Bayesian Optimized CNN-BiGRU Hybrid Neural Networks

Efficient and accurate short-term load forecast can improve the utilization rate of power generation equipment and the effectiveness of economic dispatch in the process of power market trading. In order to solve the problem of a large number of features in the historical data and the lack of obvious feature relationships, and to fully explore the connection of temporal features in load data, this paper proposes a convolutional neural network (CNN) based on Bayesian optimization-bidirectional gate recurrent unit (BiGRU) method for short-term power load forecasting. Firstly, a Pearson correlation coefficient is used for the initial screening of load features, and then a recursive feature elimination (RFE) combined with a regression model is used for the backward selection of features to complete the feature parameter screening. Moreover, a convolutional bidirectional gate recurrent unit (CNN-BiGRU) network model is constructed, and hyperparameter tuning is optimized using Bayesian optimization. The data are fed into the CNN network, which is used to extract a high-dimensional feature vector reflecting the complex change relationship between features and load, and the extracted feature vector is constructed into a time series form and fed into the BiGRU network to complete short-term load prediction. The real data sets of Nongfu Spring Company and Midea Company are taken as practical examples, according to the experimental results, the prediction accuracy of the model can reach 95.9%, which has better prediction effect compared with other models. © 2022 Science Press. All rights reserved.