Short-Term Electric Vehicle Charging Load Forecasting Based on Sample Entropy and CEEMDAN-TCN-BiGRU Hybrid Model

The large-scale integration of electric vehicles (EVs) brings significant challenges to power grid operation due to the strong nonlinearity and non-stationarity of charging loads. To address the limitations of existing methods in noise suppression and temporal feature extraction, this paper proposes a short-term EV charging load forecasting model that integrates Sample Entropy (SE) with a TCN-BiGRU hybrid network. First, Isolation Forest and KNN are used for anomaly correction and missing value imputation. Then, CEEMDAN decomposes the load series, and SE is introduced to screen out high-noise components for sequence reconstruction. The reconstructed sequence is fed into the TCN-BiGRU framework, where TCN extracts multi-scale local features and BiGRU captures bidirectional long-term dependencies. Tested on a full-year real-world charging dataset, the proposed model outperforms GRU, BiGRU, and TCN-GRU across all seasons. Compared with GRU, the proposed model reduces MAE and RMSE by 57.8% and 61.3%, respectively, in winter, while improving R2 from 0.827 to 0.978.