Short-Term Power Load Prediction of VMD-LSTM Based on ISSA Optimization

Abstract

Accurate short-term power load forecasting (STPLF) is critical for balancing electricity supply–demand and ensuring grid reliability. To address the challenges of fluctuating power loads and inaccurate predictions by conventional methods, this paper presents a novel hybrid framework combining Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM), and the Improved Sparrow Search Algorithm (ISSA). First, the power load series is decomposed into intrinsic mode functions (IMFs) via VMD, where the optimal decomposition order K is determined using permutation entropy (PE). Next, the decomposed IMFs and meteorological covariates are reconstructed into feature vectors, which are then input into the LSTM network for component-wise forecasting, and, finally, the prediction results of each component are reconstructed to obtain the final power load prediction result. The Improved Sparrow Search Algorithm (ISSA), which integrates piecewise chaotic mapping into population initialization to augment the global exploration capability, is employed to fine-tune LSTM hyperparameters, thereby enhancing the prediction precision. Finally, two case studies are conducted using Australian regional load data and Detu’an City historical load records. The experimental results indicate that the proposed model achieves reductions of 73.03% and 82.97% compared with the VMD-LSTM baseline, validating its superior predictive accuracy and cross-domain generalization capability.