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3 期                 苗   芮等:基于深度学习的多气象要素影响的供热负荷预测模型研究                                       831
               热负荷预测模型的性能, 相较于仅输入气温, 相对                             Building Technology Communication (2): 49.
               误差减小约 1. 4 %。此外, 在相同的数据条件下,                       李志新, 赖志琴, 2019. 基于 NARX 神经网络的电力负荷中期预测
                                                                    [J]. 贵州农机化, 1: 26-31. Li Z X, Lai Z Q, 2019. Medium
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               降低约3. 6 %。                                           Guizhou Agricultural Mechanization, 1: 26-31.
                                                                 鹿宇, 2022. 热力站供热负荷预测的影响因素研究[D]. 北京: 北京
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