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Accurate short-term electrical load forecasting is of great significance for the design and optimization scheduling of integrated energy systems（IES）. However，the load data in real integrated energy systems are low-quality and fluctuating，so the forecasting accuracy of existing prediction models is low. A short-term electrical load forecasting method based on attention-based long short-term memory（AT-LSTM）and Stacking learning was proposed. Under the framework of Stacking ensemble learning，AT-LSTM，random forest and decision tree were ensembled to forecast short-term electrical load which can make up for the low prediction accuracy of a single model. Based on the exploratory analysis results of data，the data feature engineering model was constructed to input features，and this prediction method was used for short-term electricity load prediction. The experimental results of the integrated energy system in Beijing show that compared to other algorithms，the proposed method has a maximum prediction error reduction of 24.8%.
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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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