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

Accurate short-term load forecasting plays a crucial role in guiding and regulating the operation of power companies. In this study, we propose a combined prediction model based on Long Short-Term Memory (LSTM) and Improved Whale Optimization Algorithm (IWOA) to enhance prediction accuracy and address the limitations of existing single methods. The parameter optimization of the LSTM model requires significant computing resources and time. To tackle this issue, we introduced the Whale Optimization Algorithm (WOA) and improved the algorithm through the roulette method to form a whale optimization algorithm that enhances its global search capability and avoids falling into local optimal solutions. Additionally, in order to handle the nonlinearity and non-smoothness of the input data of the model, we utilized fully integrated Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) in this study to improve the efficiency of model training. By combining these techniques, we constructed a new CEEMDAN-IWOA-LSTM combined prediction model. The simulation results in MATLAB demonstrate that the prediction accuracy of this model reaches 99.05 %, which surpasses other prediction models in all evaluation indexes.