Time-Lag-Aware Redundancy-Constrained Feature Selection and VMD–LSTM Framework for Short-Term Load Forecasting

In smart grids, accurate load forecasting is essential for maintaining the balance between supply and demand and ensuring the safe and stable operation of power systems. To address the strong nonstationarity of short-term load sequences, the lagged effects of meteorological factors, and variable redundancy, this study proposes a load forecast framework integrating time-lag-aware redundancy-constrained feature screening and signal decomposition. First, load and meteorological data are preprocessed. Then, key lagged meteorological features are selected by combining the Pearson correlation coefficient, mutual information, and the mRMR criterion. Furthermore, VMD is adopted to decompose the load sequence, and the decomposed modes and selected features are jointly input into the LSTM model for forecast. The results show that the proposed framework achieves high forecast accuracy, stability, and robustness under different load scenarios and electricity-consumption patterns, providing a reference for short-term load forecasting and power dispatching.