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基于ISCA-DBN的飞机地面空调能耗预测

刘涵 林家泉

刘涵,林家泉. 基于ISCA-DBN的飞机地面空调能耗预测[J]. 北京航空航天大学学报,2025,51(6):2176-2184 doi: 10.13700/j.bh.1001-5965.2023.0409
引用本文: 刘涵,林家泉. 基于ISCA-DBN的飞机地面空调能耗预测[J]. 北京航空航天大学学报,2025,51(6):2176-2184 doi: 10.13700/j.bh.1001-5965.2023.0409
LIU H,LIN J Q. Energy consumption prediction of aircraft ground air conditioning based on ISCA-DBN[J]. Journal of Beijing University of Aeronautics and Astronautics,2025,51(6):2176-2184 (in Chinese) doi: 10.13700/j.bh.1001-5965.2023.0409
Citation: LIU H,LIN J Q. Energy consumption prediction of aircraft ground air conditioning based on ISCA-DBN[J]. Journal of Beijing University of Aeronautics and Astronautics,2025,51(6):2176-2184 (in Chinese) doi: 10.13700/j.bh.1001-5965.2023.0409

基于ISCA-DBN的飞机地面空调能耗预测

doi: 10.13700/j.bh.1001-5965.2023.0409
详细信息
    通讯作者:

    E-mail:jqlin@cauc.edu.cn

  • 中图分类号: V351.3

Energy consumption prediction of aircraft ground air conditioning based on ISCA-DBN

More Information
  • 摘要:

    为提升飞机客舱使用地面空调制冷时地面空调能耗预测精度,提出一种改进正余弦算法(ISCA)优化深度置信网络(DBN)的地面空调能耗预测模型。与标准正余弦优化算法相比,ISCA提出一种改进Logistic混沌映射,提高了种群多样性;引入余弦调节因子,构建了一种新的非线性振荡调整因子,以平衡算法的全局搜索和局部寻优能力;基于变异进化思想提出一种学习策略,避免算法陷入局部最优。将ISCA-DBN模型应用于波音737-800飞机地面空调能耗预测中,与反向传播(BP)、支持向量机(SVM)、DBN等算法进行性能对比,仿真结果表明:基于ISCA-DBN的地面空调能耗预测模型在预测精度和实时性上有一定的提升。

     

  • 图 1  DBN模型结构

    Figure 1.  Model structure of DBN

    图 2  Adam-DBN内部结构

    Figure 2.  Internal structure of Adam-DBN

    图 3  不同学习率下模型收敛过程

    Figure 3.  Comparison of clustering accuracy of algorithms under different model depth conditions

    图 4  不同映射下的直方图对比

    Figure 4.  Comparison of Histogram comparison on different maps

    图 5  振幅调整因子调节过程

    Figure 5.  Adjustment process of amplitude adjustment factor

    图 6  ISCA-DBN训练流程

    Figure 6.  The training flow chart of ISCA-DBN

    图 7  优化算法迭代过程

    Figure 7.  Iterative process of optimization algorithms

    图 8  不同模型下预测值与真实值的对比

    Figure 8.  Comparison of predicted and real values on different models

    表  1  部分样本数据

    Table  1.   Part of sample data

    编号 乘客下机的
    客舱温度/℃
    乘客下机的
    客舱湿度/%
    乘客登机的
    客舱温度/℃
    乘客登机的
    客舱湿度/%
    耗电量/
    kW
    54 33.31 73.88 24.65 37.24 170.43
    55 30.11 61.45 27.66 36.48 148.00
    56 34.61 58.52 25.72 29.82 172.94
    57 30.18 63.06 24.56 32.93 168.12
    58 34.27 52.49 24.49 36.29 184.36
    下载: 导出CSV

    表  2  超参数搜索范围

    Table  2.   The search range of hyperparameters

    超参数 搜索空间
    预训练学习率 0.01~0.1
    微调学习率 0.01~0.1
    预训练次数 1~20
    微调次数 1~200
    批大小 1~256
    隐含层层数 1~3
    隐含层节点数 10~100
    下载: 导出CSV

    表  3  3种算法搜寻结果

    Table  3.   Three algorithms search results

    算法 预训练
    学习率
    微调
    学习率
    预训练
    次数
    微调
    次数
    批大小 隐含
    层层数
    隐含层
    节点数
    GA 0.018 5 0.036 2 19 192 12 3 [35,61,31]
    SCA 0.051 2 0.055 6 8 100 5 3 [82,44,28]
    ISCA 0.042 4 0.082 6 7 147 15 3 [67,46,34]
    下载: 导出CSV

    表  4  不同模型评价结果

    Table  4.   Evaluation results for different models

    模型 SME AMPE/% R2 泛化误差 时间/s
    BP 36.77 2.732 0.733 0.024 2.01
    SVM 35.31 2.831 0.743 0.023 1.33
    DBN 13.56 1.540 0.901 0.016 2.13
    GA-DBN 4.91 0.731 0.964 0.024 7.55
    SCA-DBN 4.29 0.719 0.968 0.050 6.97
    ISCA-DBN 3.27 0.706 0.976 0.012 2.78
    下载: 导出CSV
  • [1] WANG J Q, DU Y, WANG J. LSTM based long-term energy consumption prediction with periodicity[J]. Energy, 2020, 197: 117197. doi: 10.1016/j.energy.2020.117197
    [2] 王修岩, 刘艳敏, 张革文, 等. 基于改进协同PSO的神经网络飞机客舱能耗预测[J]. 系统仿真学报, 2018, 30(4): 1535-1541.

    WANG X Y, LIU Y M, ZHANG G W, et al. Prediction of aircraft cabin energy consumption based on improved cooperative PSO neural network[J]. Journal of System Simulation, 2018, 30(4): 1535-1541 (in Chinese).
    [3] 林家泉, 孙凤山, 李亚冲, 等. 基于IPSO-Elman神经网络的飞机客舱能耗预测[J]. 航空学报, 2020, 41(7): 323614. doi: 10.7527/S1000-6893.2020.23614

    LIN J Q, SUN F S, LI Y C, et al. Prediction of aircraft cabin energy consumption based on IPSO-Elman neural network[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(7): 323614 (in Chinese). doi: 10.7527/S1000-6893.2020.23614
    [4] 周璇, 林家泉. 基于改进长短时记忆网络的地面空调能耗预测[J]. 北京航空航天大学学报, 2023, 49(10): 2750-2760.

    ZHOU X, LIN J Q. Prediction of ground air conditioner energy consumption based on improved long short-term memory neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2023, 49(10): 2750-2760 (in Chinese).
    [5] 刘艳敏. 基于组合预测方法的波音737飞机客舱能耗预测研究[D]. 天津: 中国民航大学, 2017: 33-38.

    LIU Y M. Research on energy consumption prediction of Boeing 737 cabin based on combined forecasting method[D]. Tianjin: Civil Aviation University of China, 2017: 33-38 (in Chinese).
    [6] HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527-1554.
    [7] 张程, 林谷青, 黄靖, 等. 基于AMBOA-DBN结合相似日的短期光伏功率预测[J]. 太阳能学报, 2023, 44(6): 290-299.

    ZHANG C, LIN G Q, HUANG J, et al. Short-term pv power prediction based on amboa-dbn combined with similar days[J]. Acta Energiae Solaris Sinica, 2023, 44(6): 290-299 (in Chinese).
    [8] SHAO H D, JIANG H K, ZHANG H Z, et al. Electric locomotive bearing fault diagnosis using a novel convolutional deep belief network[J]. IEEE Transactions on Industrial Electronics, 2017, 65(3): 2727-2736.
    [9] HAN D Y, ZHAO N, SHI P M. A new fault diagnosis method based on deep belief network and support vector machine with Teager–Kaiser energy operator for bearings[J]. Advances in Mechanical Engineering, 2017, 9(12): 168781401774311.
    [10] 梁智, 孙国强, 李虎成, 等. 基于VMD与PSO优化深度信念网络的短期负荷预测[J]. 电网技术, 2018, 42(2): 598-606.

    LIANG Z, SUN G Q, LI H C, et al. Short-term load forecasting based on VMD and PSO optimized deep belief network[J]. Power System Technology, 2018, 42(2): 598-606 (in Chinese).
    [11] 徐先峰, 蔡路路, 张丽. 融合MLP和DBN的光伏发电预测算法[J]. 计算机工程与应用, 2021, 57(3): 266-272.

    XU X F, CAI L L, ZHANG L. Photovoltaic power generation prediction algorithm based on MLP and DBN[J]. Computer Engineering and Applications, 2021, 57(3): 266-272 (in Chinese).
    [12] 李益兵, 王磊, 江丽. 基于PSO改进深度置信网络的滚动轴承故障诊断[J]. 振动与冲击, 2020, 39(5): 89-96.

    LI Y B, WANG L, JIANG L. Rolling bearing fault diagnosis based on DBN algorithm improved with PSO[J]. Journal of Vibration and Shock, 2020, 39(5): 89-96 (in Chinese).
    [13] SHAN H T, SUN Y Y, ZHANG W J, et al. Reliability analysis of power distribution network based on PSO-DBN[J]. IEEE Access, 2020, 8: 224884-224894. doi: 10.1109/ACCESS.2020.3007776
    [14] MIRJALILI S. SCA: a sine cosine algorithm for solving optimization problems[J]. Knowledge-Based Systems, 2016, 96: 120-133. doi: 10.1016/j.knosys.2015.12.022
    [15] 徐松金, 龙文. 求解高维优化问题的改进正弦余弦算法[J]. 计算机应用研究, 2018, 35(9): 2574-2577. doi: 10.3969/j.issn.1001-3695.2018.09.003

    XU S J, LONG W. Improved sine cosine algorithm for solving high-dimensional optimization problems[J]. Application Research of Computers, 2018, 35(9): 2574-2577 (in Chinese). doi: 10.3969/j.issn.1001-3695.2018.09.003
    [16] 何庆, 徐钦帅, 魏康园. 基于改进正弦余弦算法的无线传感器节点部署优化[J]. 计算机应用, 2019, 39(7): 2035-2043. doi: 10.11772/j.issn.1001-9081.2018112282

    HE Q, XU Q S, WEI K Y. Enhanced sine cosine algorithm based node deployment optimization of wireless sensor network[J]. Journal of Computer Applications, 2019, 39(7): 2035-2043 (in Chinese). doi: 10.11772/j.issn.1001-9081.2018112282
    [17] NENAVATH H, JATOTH R K. Hybridizing sine cosine algorithm with differential evolution for global optimization and object tracking[J]. Applied Soft Computing, 2018, 62: 1019-1043. doi: 10.1016/j.asoc.2017.09.039
    [18] ATTIA A F, EL SEHIEMY R A, HASANIEN H M. Optimal power flow solution in power systems using a novel sine-cosine algorithm[J]. International Journal of Electrical Power & Energy Systems, 2018, 99: 331-343.
    [19] 李正明, 梁彩霞, 王满商. 基于PSO-DBN神经网络的光伏短期发电出力预测[J]. 电力系统保护与控制, 2020, 48(8): 149-154.

    LI Z M, LIANG C X, WANG M S. Short-term power generation output prediction based on a PSO-DBN neural network[J]. Power System Protection and Control, 2020, 48(8): 149-154 (in Chinese).
    [20] 刘帼巾, 刘达明, 缪建华, 等. 基于变分模态分解和改进灰狼算法优化深度置信网络的自动转换开关故障识别[J]. 电工技术学报, 2024, 39(4): 1221-1233.

    LIU G J, LIU D M, MIAO J H, et al. Fault identification of automatic transfer switching equipment based on VMD-WPE and IGWO optimized DBN[J]. Transactions of China Electrotechnical Society, 2024, 39(4): 1221-1233 (in Chinese).
    [21] 孙凤山, 范孟豹, 曹丙花, 等. 基于混沌映射与差分进化自适应教与学优化算法的太赫兹图像增强模型[J]. 仪器仪表学报, 2021, 42(4): 92-101.

    SUN F S, FAN M B, CAO B H, et al. The terahertz image enhancement model based on adaptive teaching-learning based optimization algorithm with chaotic mapping and differential evolution[J]. Chinese Journal of Scientific Instrument, 2021, 42(4): 92-101 (in Chinese).
    [22] 刘公致, 吴琼, 王光义, 等. 改进型Logistic混沌映射及其在图像加密与隐藏中的应用[J]. 电子与信息学报, 2022, 44(10): 3602-3609.

    LIU G Z, WU Q, WANG G Y, et al. A improved logistic chaotic map and its application to image encryption and hiding[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3602-3609 (in Chinese).
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出版历程
  • 收稿日期:  2023-06-23
  • 录用日期:  2023-08-14
  • 网络出版日期:  2023-08-25
  • 整期出版日期:  2025-06-30

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