Research and application of short-term load forecasting based on CEEMDAN-LSTM modeling

Abstract Accurate short-term load forecasts play an important role in guiding and regulating the operations of electric utilities.Using long- and short-term memory neural networks with an improved whale optimization technique, this study suggests a combination strategy for long- and short-term neural network prediction.The long-short-term memory neural network mitigates the issue of gradient vanishing and explosion, caused by the cumulative multiplication of the activation function of RNN when handling lengthy sequences.In order to address the issue of model parameter randomness, the Whale Optimization Algorithm (WOA) is introduced. The improved Whale Optimization Algorithm (IWOA) is then obtained by using the roulette method to alter the individual whale population's optimization methods in order to avoid falling into the local optimum.In this paper, the adaptive noise-complete ensemble empirical modal decomposition method CEEMDAN is introduced to solve the problem of model training efficiency caused by the nonlinear non-smoothness of the model input data, so as to construct a combined CEEMDAN-IWOA-LSTM prediction model based on CEEMDAN-IWOA-LSTM.The results show that the model's prediction accuracy reaches 99.05%, and various prediction and assessment indexes are better than other prediction models, with the best performance and effect.