| 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
|
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.
| [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]. 北京亚洲成人在线一二三四五六区学报, 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).
|
Figures(8) / Tables(4)
Copyright © Journal of Beijing University of Aeronautics and Astronautics
Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@cqjj8.com
Supported by:
Beijing Renhe Information Technology Co., Ltd.
DownLoad:
