Development and application of an evolutionary deep learning framework of LSTM based on improved grasshopper optimization algorithm for short-term load forecasting

Accurate short-term load forecasting (STLF) plays an important role in the daily operation of a smart grid. In order to forecast short-term load more effectively, this article proposes an integrated evolutionary deep learning approach based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), improved grasshopper optimization algorithm (IGOA), and long short-term memory (LSTM) network. First of all, CEEMDAN is used to decompose the original data into a certain number of periodic intrinsic mode functions (IMFs) and a residual. Secondly, the nonlinear strategy is used to improve the attenuation coefficient of GOA, and the golden sine operator is introduced to update the individual position of GOA. Then the improved GOA is used to optimize the parameters of the LSTM model, which are the number of hidden neurons and learning rate. The optimized LSTM is applied to the decomposed modal components. Finally, the prediction results of each modal component are aggregated to get the real STLF results. Through comparative experiments, the effectiveness of the CEEMDAN method, the IGOA method, and the combined model is verified, respectively. The experimental results show that the integrated evolutionary deep learning method proposed in this article is an effective tool for STLF. • A new evolutionary deep learning short-term load prediction method. • Using CEEMDAN to decompose the load sequence into relatively simple subsequences. • Nonlinear strategy and golden sine operator are used to improve GOA. • Using the improved GOA to optimize the parameters of LSTM. • The superiority of the proposed model is verified by comparing the model.