Assessment and improvement of elevator controllers for energy efficiency

Hasan, Muhammad; Fink, Rainer; Suyambu, Muthuvel Raj; Baskaran, Manoj Kumar

doi:10.1109/isce.2012.6241747

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…The duty of the EGCS is to meet all the demands by assigning an elevator to each landing call (call made at a floor) while optimizing several criteria. These criteria can be service oriented, such as the average wait time (AWT) or the percentage of calls that have a long wait time (also called long-waiting call percentages, which are usually defined as the percentages of calls that require more than a given number of seconds), or oriented toward the preferences of the tenants, such as energy consumption preferences [5], [6]. The EGCS performance is measured according to the values of these parameters.…”

Section: Definition Of An Egcs and The Landing-call Allocation Problemmentioning

confidence: 99%

A Survey of Elevator Group Control Systems for Vertical Transportation: A Look at Recent Literature

Fernández

Cortés

2015

IEEE Control Syst.

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Section: Definition Of An Egcs and The Landing-call Allocation Problemmentioning

confidence: 99%

A Survey of Elevator Group Control Systems for Vertical Transportation: A Look at Recent Literature

Fernández

Cortés

2015

IEEE Control Syst.

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“…In this case, selecting the right elevator is crucial for optimizing the performance parameters of EGCS. The major performance indicators of EGCS are average waiting-time (AWT), average journey-time (AJT), computational time (CT), and energy consumption (EC) [2][3][4][5]. Due to the contradictory relation among these performance indices, it is difficult to satisfy all the parameters at the same time [1].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Particle Swarm Optimization and Genetic Algorithm in Energy-Saving Elevator Group Control System

Hanif

Mohammad

2021

Lecture Notes on Data Engineering and Communications Technologies

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Optimization of energy consumption in Elevator Group Control System (EGCS) has become a concerning issue due to the global energy crisis. To resolve this concern, only a handful amount of approaches, based on swarm intelligence, have been implemented. For this reason, this study implements and analyzes the energy-saving EGCS based on two popular metaheuristic algorithms: Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The performance analysis of these two algorithms in energy-saving EGCS reveals that both of the algorithms have some pros and cons. While PSO can optimize energy consumption much better than GA in most cases, the trapping in local minima or pre-mature convergence of PSO makes its performance worse. GA, on the other hand, is unable to reduce energy consumption in EGCS to a considerably lower level. However, the average energy consumption and standard deviation of GA are superior to that of PSO. In the case of computational time, PSO outperforms GA, which makes PSO a popular choice where faster computation is required.

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Development of an Elevator Energy Consumption Monitoring Device

Lin

Wang

et al. 2015

Proceedings of the Second International Conference on Mechatronics and Automatic Control

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Assessment and improvement of elevator controllers for energy efficiency

Cited by 7 publications

References 6 publications

A Survey of Elevator Group Control Systems for Vertical Transportation: A Look at Recent Literature

A Survey of Elevator Group Control Systems for Vertical Transportation: A Look at Recent Literature

Performance Analysis of Particle Swarm Optimization and Genetic Algorithm in Energy-Saving Elevator Group Control System

Development of an Elevator Energy Consumption Monitoring Device

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