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Prediction and Analysis of elevator traffic flow under the LSTM Neural Network

Intelligent Control and Automation 2024年第2期15卷 63-82页

作者： Mo Shi Entao Sun Xiaoyan Xu Yeol Choi School of Architecture Kyungpook National University Daegu Republic of Korea School of Software Engineering East China Normal University Shanghai China HaXell elevator Co. Ltd. Shanghai China

elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion within elevator systems. Many passengers experience dissatisfaction with prolonged wait times, leading to impatience and frustration among building occupants. The widespread adoption of neural networks and deep learning technologies across various fields and industries represents a significant paradigm shift, and unlocking new avenues for innovation and advancement. These cutting-edge technologies offer unprecedented opportunities to address complex challenges and optimize processes in diverse domains. In this study, LSTM (Long Short-Term Memory) network technology is leveraged to analyze elevator traffic flow within a typical office building. By harnessing the predictive capabilities of LSTM, the research aims to contribute to advancements in elevator group control design, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments. The findings of this research have the potential to reference the development of intelligent elevator management systems, capable of dynamically adapting to fluctuating passenger demand and optimizing elevator usage in real-time. By enhancing the efficiency and functionality of vertical transportation systems, the research contributes to creating more sustainable, accessible, and user-friendly living environments for individuals across diverse demographics.

关键词： elevator traffic flow Neural Network LSTM elevator Group Control

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A Multi-mode Prediction Method for elevator traffic flow Based on Classification Off-line

A Multi-mode Prediction Method for Elevator Traffic Flow Bas...

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第二十七届中国控制会议

作者： Zong Qun 1,Wang Weijia 1,Shang Anna 2 1.School of Electrical Engineering and Automation,Tianjin University,Tianjin 300072,P.R.China 2.Department of Electrical Engineering,Shaanxi University of Technology,Hanzhong Shaanxi 723001,P.R.China

In order to achieve the on-line traffic flow prediction,a novel multi-mode prediction method based on mode classification off-line is ***,the elevator traffic flow is classified off-line into patterns by the two-stage Clustering Algorithm,Artificial Immune C-Means Clustering Algorithm（AI C-Means CA） .Then Gaussian Mixture Model（GMM） is used to model the multi-mode elevator traffic *** the EM algorithm is utilized to estimate the parameters of GMM to predict elevator traffic flow on ***,the effectivity of this prediction method is validated by comparing simulation results with other prediction methods.

关键词： elevator traffic flow Multi-mode Prediction Artificial Immune C-Means Clustering Algorithm Gaussian Mixture Model EM Algorithm

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Chaos Characteristics of Peak elevator traffic flow

Chaos Characteristics of Peak Elevator Traffic Flow

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第三十届中国控制会议

作者： LI Jun-Fang~(1,2),LENG Jian-Wei~2,ZHANG Jing-Long~1 1.School of Electrical Engineering and Automation,Tianjin University,Tianjin 300072,P.R.China 2.School of Electrical Engineering,Tianjin University of Technology,Tianjin 300384,P.R.China

＜正＞In this paper,nonlinear time series techniques are applied to analyze the peak elevator traffic flow *** phase space,which describes the evolution of the behaviour of a nonlinear system,is reconstructed using the delay embedding theorem suggested by *** embedding parameters,*** delay time and the embedding dimension are estimated using the mutual information of the data and the false nearest neighbor algorithm,*** the attractor of the elevator traffic flow from reconstruction is not necessarily sufficient indication of chaos,therefore we then calculate the correlation dimension of the resulting attractor and the largest Lyapunov *** is demonstrated that the traffic flows of the up-peak and down-peak all exhibit low-dimensional chaotic *** result will help to adjust the group control scheduling methods according to the chaotic behaviour of the peak flow so as to increase the performance index.

关键词： elevator traffic flow Chaos Phase Space Reconstruction Largest Lyapunov Exponent

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Low dimensional chaos in elevator peak traffic

Low dimensional chaos in elevator peak traffic

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2011年中国智能自动化学术会议

作者：李俊芳宗群张景龙王芳 School of Electrical Engineering and Automation Tianjin University School of Electrical Engineering Tianjin University of Technology

Nonlinear time series techniques were applied to analyze up-peak and down-peak traffic flow data and energy consumption data in elevator *** space was reconstructed using the delay embedding theorem,which described the behaviour evolution of a nonlinear *** parameters including delay time and embedding dimension were estimated using the mutual information of the data and the false nearest neighbour algorithm,*** to the attractor of the elevator traffic flow data and energy consumption data from reconstruction was not a necessarily sufficient indication of chaos,the correlation dimension and the largest Lyapunov exponent aiming at the above data were *** on these analysis and calculation,the results indicate that low dimensional chaotic characteristics obviously exist in the up-peak and down-peak traffic flow data and energy consumption *** result helps to adjust the group control scheduling methods according to the chaotic behaviour of the peak flow and the building energy consuming to increase the performance index of the system.

关键词： elevator traffic flow energy consumption chaos phase space reconstruction largest lyapunov exponent

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