The paper covers analysis and investigation of lighting automation system in low-traffic long-roads. The main objective is to provide optimal solution between expensive safe design that utilizes continuous street ligh...The paper covers analysis and investigation of lighting automation system in low-traffic long-roads. The main objective is to provide optimal solution between expensive safe design that utilizes continuous street lighting system at night for the entire road, or inexpensive design that sacrifices the safety, relying on using vehicles lighting, to eliminate the problem of high cost energy consumption during the night operation of the road. By taking into account both of these factors, smart lighting automation system is proposed using Pattern Recognition Technique applied on vehicle number-plates. In this proposal, the road is sectionalized into zones, and based on smart Pattern Recognition Technique, the control system of the road lighting illuminates only the zone that the vehicles pass through. Economic analysis is provided in this paper to support the value of using this design of lighting control system.展开更多
Accurate prediction of electric vehicle(EV)charging duration is critical for improving user satisfaction and enabling efficient real-time charging management.This paper proposes a dynamic charging duration prediction ...Accurate prediction of electric vehicle(EV)charging duration is critical for improving user satisfaction and enabling efficient real-time charging management.This paper proposes a dynamic charging duration prediction framework for EVs,composed of four coordinated modules:data preprocessing,charging pattern classification,static prediction,and dynamic bias correction.First,raw charging data collected from the Battery Management System(BMS)is cleaned and normalized to address missing and abnormal values.An enhanced convolutional autoencoder(EV-CAE)is then employed to extract multi-scale temporal features,while K-Means clustering is used to identify representative charging behavior patterns.Based on the classified patterns,the static prediction module estimates the current charging duration by leveraging historical data and pattern labels.To enhance adaptability under dynamic conditions,a bias correction mechanism is designed,integrating linear,logarithmic,proportional,and deep learning-based strategies to adjust the prediction results in real time.Experimental results on real-world EV datasets demonstrate that the proposed framework significantly improves prediction accuracy.In particular,the dynamic correction module increases the coefficient of determination(R^(2))from 0.948 to 0.960,while maintaining robust performance under fluctuating charging behavior and low-temperature conditions.展开更多
文摘The paper covers analysis and investigation of lighting automation system in low-traffic long-roads. The main objective is to provide optimal solution between expensive safe design that utilizes continuous street lighting system at night for the entire road, or inexpensive design that sacrifices the safety, relying on using vehicles lighting, to eliminate the problem of high cost energy consumption during the night operation of the road. By taking into account both of these factors, smart lighting automation system is proposed using Pattern Recognition Technique applied on vehicle number-plates. In this proposal, the road is sectionalized into zones, and based on smart Pattern Recognition Technique, the control system of the road lighting illuminates only the zone that the vehicles pass through. Economic analysis is provided in this paper to support the value of using this design of lighting control system.
基金supported by Science and Technology Innovation Key R&D Program of Chongqing(CSTB2023TIAD-STX0024)the Science and Technology Research Program of Chongqing Municipal Education Commission(Grant number KJQN202201121).
文摘Accurate prediction of electric vehicle(EV)charging duration is critical for improving user satisfaction and enabling efficient real-time charging management.This paper proposes a dynamic charging duration prediction framework for EVs,composed of four coordinated modules:data preprocessing,charging pattern classification,static prediction,and dynamic bias correction.First,raw charging data collected from the Battery Management System(BMS)is cleaned and normalized to address missing and abnormal values.An enhanced convolutional autoencoder(EV-CAE)is then employed to extract multi-scale temporal features,while K-Means clustering is used to identify representative charging behavior patterns.Based on the classified patterns,the static prediction module estimates the current charging duration by leveraging historical data and pattern labels.To enhance adaptability under dynamic conditions,a bias correction mechanism is designed,integrating linear,logarithmic,proportional,and deep learning-based strategies to adjust the prediction results in real time.Experimental results on real-world EV datasets demonstrate that the proposed framework significantly improves prediction accuracy.In particular,the dynamic correction module increases the coefficient of determination(R^(2))from 0.948 to 0.960,while maintaining robust performance under fluctuating charging behavior and low-temperature conditions.
