北京城市代谢预测研究——基于长短期记忆神经网络模型
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刘炳春, 齐鑫, 王庆山
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Urban metabolism prediction of Beijing City based on long short-term memory neural network
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Bingchun LIU, Xin QI, Qingshan WANG
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| 表1 不同部门体外能代谢率预测结果 |
| Tab.1 Prediction results of exosomatic energy metabolic rate (EMR) in different sectors |
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| 年份 | 第一产业 | | 第二产业 | | 第三产业 | | 生活部门 | | 总体 | | EMR1/(MJ/h) | 增速/% | EMR2/(MJ/h) | 增速/% | EMR3/(MJ/h) | 增速/% | EMR4/(MJ/h) | 增速/% | | EMRT/(MJ/h) | 增速/% | | 2017 | 22.36 | 1.09 | 143.31 | 1.3 | 52.04 | 2.08 | 2.89 | 2.62 | 10.29 | -4.94 | | 2018 | 22.11 | -1.1 | 145.26 | 1.36 | 52.8 | 1.46 | 2.99 | 3.16 | 10 | -2.77 | | 2019 | 22.08 | -0.15 | 147.23 | 1.36 | 53.63 | 1.57 | 3.09 | 3.37 | 9.55 | -4.5 | | 2020 | 21.95 | -0.61 | 149.23 | 1.36 | 54.47 | 1.56 | 3.21 | 4.02 | 9.16 | -4.13 | | 2021 | 21.86 | -0.41 | 151.26 | 1.36 | 55.33 | 1.57 | 3.35 | 4.26 | 8.69 | -5.08 | | 2022 | 21.75 | -0.51 | 153.32 | 1.36 | 56.2 | 1.59 | 3.51 | 4.94 | 8.21 | -5.47 |
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