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To address the limitations of traditional gas consumption prediction methods in dealing with complex time series data, a combined model (CNN-BiLSTM-Attention) integrating convolutional neural networks, bidirectional long short-term memory networks, and attention mechanisms is proposed for urban gas consumption prediction. An empirical analysis was conducted on the actual gas consumption situation of a city in the western region. The results show that the root mean square error, mean absolute error, and coefficient of determination of this model are 19.14, 17.53 and 0.966 6 respectively, and its prediction effect is significantly better than that of other models. The research indicates that the CNN-BiLSTM-Attention network model provides an effective solution for urban gas consumption prediction and offers a scientific basis for urban energy management and decision-making.
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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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