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ISCADBN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。
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 airconditioning 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.
作者
刘涵
林家泉
LIU Han;LIN Jiaquan(College of Electronic Information and Automation,Civil Aviation University of China,Tianjin 300300,China)
出处
《北京航空航天大学学报》
北大核心
2025年第6期2176-2184,共9页
Journal of Beijing University of Aeronautics and Astronautics
关键词
飞机客舱
地面空调
能耗预测
正余弦优化
深度置信网络
aircraft cabin
ground air condition
energy demands prediction
sine cosine algorithm
deep belief network
