An Improved Self-Organizing Migration Algorithm for Short-Term Load Forecasting with LSTM Structure Optimization

Abstract

Establishing an accurate and robust short-term load forecasting (STLF) model for a power system in safe operation and rational dispatching is both required and beneficial. Although deep long short-term memory (LSTM) networks have been widely used in load forecasting applications, it still has some problems to optimize, such as unstable network performance and long optimization time. This study proposes an adaptive step size self-organizing migration algorithm (AS-SOMA) to improve the predictive performance of LSTM. First, an optimization model for LSTM prediction is developed, which divides the LSTM structure seeking into two stages. One is the optimization of the number of hidden layer layers, and the other optimizes the number of neurons, time step, learning rate, epochs, and batch size. Then, a logistic chaotic mapping and an adaptive step size method were proposed to overcome slow convergence problems and stacking into local optimum of SOMA. Comparison experiments with SOMA, PSO, CPSO, LSOMA, and OSMA on test function sets show the advantages of the improved algorithm. Finally, the AS-SOMA-LSTM network prediction model is used to solve the STLF problem to verify the effectiveness of the proposed algorithm. Simulation experiments show that the AS-SOMA exhibits higher accuracy and convergence speed on the standard test function set and has strong prediction ability in STLF application with LSTM.