Short-term load forecasting based on CEEMDAN and dendritic deep learning

Abstract

Short-term load forecasting (STLF) plays a key role in improving the operational efficiency of real-time electricity markets by reducing load uncertainty, optimising the allocation of generation resources and increasing the reliability of electricity supply. Despite significant advances in the field, achieving high accuracy in STLF remains a challenge due to the inherent complexity and volatility of load data. This study proposes a novel STLF method that synergistically combines the strengths of a dendritic neuron model (DNM), long short-term memory, and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to address these challenges. First, historical load data is decomposed into components of different frequencies using CEEMDAN. Then, LSTM is used to extract the temporal characteristics of these load subsequence components in combination with various influencing factors such as temperature and humidity. Uniquely, this study innovatively integrates a dendritic neuron model instead of the traditional fully connected layer, a structural innovation that enhances the model’s ability to explore the intrinsic correlations of the time series data in depth. This allows the model to process and predict each component individually, and then later reconstruct and sum these independent prediction results to obtain a final prediction value with higher accuracy. Experiments are conducted on the Panama electricity load dataset to compare the proposed dendritic learning model with several state-of-the-art models, including CrossFormer and Transformer. The results confirm that the proposed model has significantly better prediction accuracy.