基于ISCA-DBN的飞机地面空调能耗预测

刘涵; 林家泉

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

刘涵,
林家泉^,

中国民航大学电子信息与自动化学院，天津 300300

详细信息

通讯作者:
E-mail：jqlin@cauc.edu.cn

中图分类号: V351.3
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- 文章访问数: 228
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- 被引次数: 0
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

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

LIU Han,
LIN Jiaquan^,

College of Electronic Information and Automation，Civil Aviation University of China，Tianjin 300300，China

More Information

Corresponding author: E-mail：jqlin@cauc.edu.cn

摘要

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。
- 飞机客舱 /
- 地面空调 /
- 能耗预测 /
- 正余弦优化 /
- 深度置信网络
Abstract:
An improved sine-cosine optimization (ISCA) deep belief network (DBN) prediction model for ground air conditioning energy consumption is suggested in order to increase the prediction accuracy of ground air conditioning energy consumption when the aircraft cabin is cooled by ground air conditioning. In contrast to the standard sine-cosine optimization algorithm, the improved sine-cosine algorithm introduces a cosine adjustment factor to create a new non-linear oscillation adjustment factor to balance the algorithm's overall performance. It also suggests an improved logistic chaotic map, which increases population diversity. In order to prevent the algorithm from reaching a local optimum, a learning technique based on the concept of mutation evolution is finally suggested.Search and local optimization capabilities; finally, a learning strategy is proposed based on the idea of mutation evolution to avoid the algorithm from falling into local optimum. The ISCA-DBN model is applied to the prediction of ground air-conditioning energy consumption of Boeing 737-800 aircraft, and the performance is compared with back propagation (BP)、support vector machine (SVM)、DBN algorithms. There is a certain improvement in both prediction accuracy and real-time performance.
- aircraft cabin /
- ground air condition /
- energy demands prediction /
- sine cosine algorithm /
- deep belief network

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图 1 DBN模型结构

Figure 1. Model structure of DBN

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图 2 Adam-DBN内部结构

Figure 2. Internal structure of Adam-DBN

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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

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图 4 不同映射下的直方图对比

Figure 4. Comparison of Histogram comparison on different maps

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图 5 振幅调整因子调节过程

Figure 5. Adjustment process of amplitude adjustment factor

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图 6 ISCA-DBN训练流程

Figure 6. The training flow chart of ISCA-DBN

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图 7 优化算法迭代过程

Figure 7. Iterative process of optimization algorithms

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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

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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

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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

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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]

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表 4 不同模型评价结果

Table 4. Evaluation results for different models

模型	S_ME	A_MPE/%	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

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