Multi-step electric vehicles charging loads forecasting: An autoformer variant with feature extraction, frequency enhancement, and error correction blocks

The extensive integration of electric vehicles (EVs) into power grids has raised safety concerns. Meanwhile current EVs charging loads forecasting models mainly focus on single-step predictions, neglect load sparsity and underutilize residuals, limiting their accuracy. To tackle these issues, a multi-step short-term forecasting model, specifically designed to predict charging loads at public EVs charging stations using historical data, is proposed. The model begins with a preprocessing phase to handle missing data and outliers. Subsequently, an Autoformer variant is intruduced, which initially integrates feature extraction block leveraging the Temporal Convolutional Network (TCN) to adeptly capture temporal dynamics. This is further enhanced with a Discrete Cosine Transform (DCT)-based frequency enhancement block to learn sparsity and periodicity features of EVs charging loads by frequency mapping. In addition, an advanced error correction block, comprising Bayesian optimization algorithm, Convolutional Neural Networks, Multi-Head Self-Attention, and Gated Recurrent Units (BCMG), is incorporated to refine initial prediction results. Above all, the TCN-DCT enhanced Autoformer-BCMG (TDformerBCMG) model is proposed. Finally, a comprehensive experimental analysis is conducted on ACN-data for EVs charging load forecasting in 1–3 h steps. It is demonstrated that TDformerBCMG not only is effective and superior in each component, but also achieves a reduction in Mean Absolute Error by 20.94 % to 74.05 % compared to existing models. Moreover, stability is evidenced across 50 repeated trials, with the standard deviation for the Coefficient of Determination varying between 8.5844e-3 and 3.1580e-2. Further discussion on application analysis is provided. Consequently, TDformerBCMG offers a viable methodology and establishes a new benchmark for power system implementation.