Short-Term Load Forecasting Based on EEMD-WOA-LSTM Combination Model

Abstract

The purpose of this study was to better apply artificial intelligence algorithm to load forecasting and effectively improve the forecasting accuracy. Based on the long short-term memory neural networks, a combined model based on whale bionic optimization is proposed for short-term load forecasting. The whale bionic algorithm is used to solve the problem that the long short-term memory neural networks are easy to fall into local optimization and improve the accuracy of parameter optimization. The original signal is decomposed into multiple characteristic components by set empirical mode decomposition. Each feature component is input into the bionic optimized combination model for prediction. Finally, get the load forecasting results. Compared with the prediction results of EEMD-ARMA model, RNN model, LSTM model, and WOA-LSTM model, the combined prediction model optimized by whale bionics has less prediction error and higher prediction accuracy.

A Short-Term Load Forecasting Model Based on Crisscross Grey Wolf Optimizer and Dual-Stage Attention Mechanism

“…(2) Weight normalization: In addition, f t is normalized using the softmax function, as shown in Equation (14).…”

“…The experimental results show that the regression accuracy and generalization ability of the model have been improved by the proposed algorithm. To address the problem of the Energies 2023, 16, 2878 2 of 24 ease in which long short-term memory neural networks fall into local minima, a whale optimization algorithm (WOA) is used to optimize the network [14]. Li et al [15] use grey wolf optimization (GWO) to optimize the parameters of every single kernel in an extreme learning machine to improve its forecasting ability.…”

Short-term power load forecasting model based on multi-strategy improved WOA optimized LSTM

“…To address the problem that long and short-term memory neural networks tend to fall into local minima, Santra et al [22] proposed a combined model of genetic algorithm (GA) and LSTM. Shao, et al [23] used the whale optimization algorithm (WOA) to optimize the LSTM network parameters. There is a significant improvement in the prediction results of both the artificially set network parameters and the network model compared to the LSTM alone.…”