Energy Saving of Elevator Group under Up-Peak Flow Based on Geese-PSO

Wang, Chunzhi; Hu, Ming; Chen, Hongwei

doi:10.1109/ccbd.2016.049

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…The core of elevator group control system-the dispatching strategy, directly determines the overall performance of elevator group control system [5]. In the past 20 years, under the continuous optimization of elevator scheduling control algorithms, it has not only shortened the waiting time of passengers, but also gradually become optimized according to different buildings, different passengers, different working modes and other different goals [6].…”

Section: Introductionmentioning

confidence: 99%

Research on multi-objective-multi-elevator coordinative strategy based on neural network

Zhang,

Chen,

Gao

et al. 2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

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With rapid development of industry, more and more attention has been paid to the effective control of elevator in highrising buildings. This paper studied the elevator control problem, proposed a multi-objective-multi-elevator cooperative scheduling strategy based on the BP neural network, constructed the BP neural network model with four dimensional indexes such as time, energy consumption, comfort level and efficiency, established the elevator's dispatching decision model and obtained the optimal strategy in each period of a day. Finally, the accuracy and feasibility of the model are verified by taking six ten-floor elevators as the specific control objects. The verification results shows that the proposed algorithm is accurate, efficient and flexible.

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Section: Introductionmentioning

confidence: 99%