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With the continuous expansion of electric vehicle charging stations, the power grid faces increasing risks such as power overload, load fluctuations, and uneven demand distribution. To address these issues, this paper proposes an Hybrid Deep Fusion(HDF)-Long Short-Term Memory(LSTM)-based method for load forecasting and graded early warning. The method integrates LSTM, Gated Recurrent Unit(GRU), and Transformer architectures with multi-source data, including historical load, meteorological conditions, and traffic flow. Pearson correlation analysis and a dynamic weight allocation mechanism are employed to improve nonlinear feature representation. Based on the transformer capacity and simultaneity factor specified in the Code for Design of Electric Vehicle Charging Stations, a three-level early warning mechanism is developed for rapid alerting near critical thresholds. Results show that the proposed model outperforms eXtreme Gradient Boosting(XGBoost), GRU, LSTM, and Transformer models, with an Mean Squared Error(MSE) of 0.185 2, an Mean Absolute Error(MAE) of 0.2682, and an R2 of 0.985 7. The model also shows good computational efficiency and application potential in charging station load forecasting and operational risk warning.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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