The application of deep learning algorithms in power system load forecasting

This article explores the application of deep learning (DL) algorithms in power system load forecasting. With the continuous advancement of the construction of new power systems, traditional load forecasting models designed based on time series analysis are no longer in line with the actual needs of the current power system. To improve the accuracy of load forecasting, this paper attempts to transform time series data into images for analysis, and then use deep learning methods widely used in the field of image processing for power load forecasting. Convolutional neural network (CNN), as a commonly used image processing algorithm, has been applied in time series data processing. However, data is still processed as a sequence matrix, and the advantages of CNN algorithm in processing image matrices have not been fully utilized. Therefore, this article proposes a CNN algorithm based on sequence to image conversion (STI-CNN). Firstly, the load sequence is converted into a load image. Then, a dual branch deep network model is used to accurately cluster the input data. Finally, the STI-CNN model is used for load prediction. Matlab simulation experiments show that the proposed STI-CNN model has excellent performance in different prediction metrics and has higher prediction accuracy.