针对自动化立体车库车辆存取能耗高的问题,以保证车库运行效率为前提,以降低系统运行能耗为目标,利用SVM支持向量机对车辆停留时间范围进行预测.以多色集合作为理论依据,将车辆质量、车辆停留时间范围和车位能耗作为特征,对立体车库车...针对自动化立体车库车辆存取能耗高的问题,以保证车库运行效率为前提,以降低系统运行能耗为目标,利用SVM支持向量机对车辆停留时间范围进行预测.以多色集合作为理论依据,将车辆质量、车辆停留时间范围和车位能耗作为特征,对立体车库车位进行分区分配.在此基础上建立车库运行模型,并以人均等待时间、人均能耗等作为输出结果对该模型进行验证.采用MATLAB软件编写仿真程序,通过比较该车位分配和车位就近分配的程序运行结果,证明该车位分配在降低车库运行能耗上的有效性.仿真结果表明:该车位分区分配模型能够保证车库运行效率,使每天车库人均运行能耗比就近原则减少5.10 k J,降低了9%.展开更多
Based on grey neural network and particle swarm optimization algorithm,an automated stereo garage decision model is proposed to solve the problems of long waiting queue and low efficiency of automated parking garage.T...Based on grey neural network and particle swarm optimization algorithm,an automated stereo garage decision model is proposed to solve the problems of long waiting queue and low efficiency of automated parking garage.The gray neural network is used to forecast the stay time of the vehicle and particle swarm optimization algorithm is used to allocate the parking spaces in the stereo garage.The proposed stereo garage mathematical model is established on condition that vehicle arrival interval obeys Poisson distribution.The performance of stereo garage is evaluated by the average waiting time,average waiting queue length,average service time and average energy consumption of the customers.By comparing the efficiency indexes of the existing model based on near-distribution principle and the proposed model based on gray neural network and particle swarm algorithm,it is proved that the proposed model based on gray neural network and particle swarm algorithm is effective in improving the efficiency of garage operation and reducing the energy consumption of garage.展开更多
文摘针对自动化立体车库车辆存取能耗高的问题,以保证车库运行效率为前提,以降低系统运行能耗为目标,利用SVM支持向量机对车辆停留时间范围进行预测.以多色集合作为理论依据,将车辆质量、车辆停留时间范围和车位能耗作为特征,对立体车库车位进行分区分配.在此基础上建立车库运行模型,并以人均等待时间、人均能耗等作为输出结果对该模型进行验证.采用MATLAB软件编写仿真程序,通过比较该车位分配和车位就近分配的程序运行结果,证明该车位分配在降低车库运行能耗上的有效性.仿真结果表明:该车位分区分配模型能够保证车库运行效率,使每天车库人均运行能耗比就近原则减少5.10 k J,降低了9%.
基金Natural Science Foundation of Gansu Province(No.1506RJZA073)Construction Science and Technology Project of Gansu Province(No.JK2016-1021605)
文摘Based on grey neural network and particle swarm optimization algorithm,an automated stereo garage decision model is proposed to solve the problems of long waiting queue and low efficiency of automated parking garage.The gray neural network is used to forecast the stay time of the vehicle and particle swarm optimization algorithm is used to allocate the parking spaces in the stereo garage.The proposed stereo garage mathematical model is established on condition that vehicle arrival interval obeys Poisson distribution.The performance of stereo garage is evaluated by the average waiting time,average waiting queue length,average service time and average energy consumption of the customers.By comparing the efficiency indexes of the existing model based on near-distribution principle and the proposed model based on gray neural network and particle swarm algorithm,it is proved that the proposed model based on gray neural network and particle swarm algorithm is effective in improving the efficiency of garage operation and reducing the energy consumption of garage.
