Short-Term Load Forecasting Based on Pelican Optimization Algorithm and Dropout Long Short-Term Memories–Fully Convolutional Neural Network Optimization

In order to improve the prediction accuracy of short-term power loads in a power system, this paper proposes a short-term load prediction method (POA-DLSTMs-FCN) based on a combination of multi-layer lost long short-term memory (DLSTM) neural networks, fully convolutional neural networks (FCNs) and the pelican optimization algorithm (POA). This method firstly uses DLSTMs to extract the time-series features of the load data, which can effectively capture the dynamic changes in the time series; subsequently, it combines the convolution operation of FCNs to obtain high-resolution information between the load data and the features, which enhances the expressive ability of the model. Through a parallel structure, DLSTMs and FCNs can jointly optimize the information extraction and then construct a more accurate load forecasting model. In addition, the learning rate, the number of hidden neurons and the deactivation probability of the Dropout layer in DLSTMs are optimized by the POA to further enhance the performance of the model. The experimental results show that the proposed optimization method has significant advantages over traditional DLSTMs and FCN-LSTM models in terms of prediction accuracy and stability.