Smart Building’s Elevator with Intelligent Control Algorithm based on Bayesian Networks

Bapin, Yerzhigit; Zarikas, Vasilios

doi:10.14569/ijacsa.2019.0100203

Cited by 17 publications

(16 citation statements)

References 31 publications

(25 reference statements)

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“…The results demonstrated that the suggested algorithm demonstrated the intended behaviour in 94% of the circumstances examined. [8] Z. Yang and W. Yue investigated Elevator Traffic Pattern Recognition Using Fuzzy BP Neural Network with Self-Organizing Map (SOM) Algorithm in 2017. SOM is a form of unsupervised learning network method that operates on the premise of grouping comparable inputs on the same output in order to find a better clustering centre after a sufficient number of repetitions.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigation of Optimization Techniques on the Elevator Dispatching Problem

Ahmed¹,

Shekha²,

Skran³

et al. 2022

Computer Networks &Amp; Communications Trends

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In the elevator industry, reducing passenger journey time in an elevator system is a major aim. The key obstacle to optimising elevator dispatching is the unpredictable traffic flow of passengers. To address this difficulty, two main features must be optimised: waiting time and journey time. To address the problem in real time, several strategies are employed, including Simulated Annealing (SA), Genetic Algorithm (GA), Particle Swarm Optimization Algorithm (PSO), and Whale Optimization Algorithm (WOA). This research article compares the algorithms discussed above. To investigate the functioning of the algorithms for visualisation and insight, a case study was created. In order to discover the optimum algorithm for the elevator dispatching problem, performance indices such as average and ideal fitness value are generated in 5 runs to compare the outcomes of the methods. The goal of this study is to compute a dispatching scheme, which is the result of the algorithms, in order to lower the average trip time for all passengers. This study builds on previous studies that recommended ways to reduce waiting time. The proposed technique reduces average wait time, improves lift efficiency, and improves customer experience.

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Section: Literature Reviewmentioning

confidence: 99%

Investigation of Optimization Techniques on the Elevator Dispatching Problem

Ahmed¹,

Shekha²,

Skran³

et al. 2022

Computer Networks &Amp; Communications Trends

View full text Add to dashboard Cite

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“…BNs have already been used in a variety of different engineering fields (Bapin and Zarikas, 2019a;Amrin et al, 2018;Zarikas et al, 2018;Papanikolaou et al, 2017;Zarikas et al, 2015;Zarikas et al, 2013;Zarikas, 2007;Stephanidis et al, 2005;Yang et al, 2019;Gao and Dong, 2019;Jha et al, 2019;Tselykh et al, 2018;Nakasima-L opez et al, 2018;Ung, 2018;Quinn et al, 2017;Yang and Xu, 2017;Sanchez et al, 2017), yet only a few works use BNs for the power system reliability assessment or probabilistic quantification of power generating reserve capacity. Yu et al (1999) is one of the first works, which uses BNs to evaluate reliability of a power grid.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic estimation of spinning reserves in smart grids with Bayesian-driven reserve allocation adjustment algorithm

Bapin

Zarikas

2021

IJESM

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Purpose This study aims to introduce a methodology for optimal allocation of spinning reserves taking into account load, wind and solar generation by application of the univariate and bivariate parametric models, conventional intra and inter-zonal spinning reserve capacity as well as demand response through utilization of capacity outage probability tables and the equivalent assisting unit approach. Design/methodology/approach The method uses a novel approach to model wind power generation using the bivariate Farlie–Gumbel–Morgenstern probability density function (PDF). The study also uses the Bayesian network (BN) algorithm to perform the adjustment of spinning reserve allocation, based on the actual unit commitment of the previous hours. Findings The results show that the utilization of bivariate wind prediction model along with reserve allocation adjustment algorithm improve reliability of the power grid by 2.66% and reduce the total system operating costs by 1.12%. Originality/value The method uses a novel approach to model wind power generation using the bivariate Farlie–Gumbel–Morgenstern PDF. The study also uses the BN algorithm to perform the adjustment of spinning reserve allocation, based on the actual unit commitment of the previous hours.

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“…One of the earliest works describing utilization of video cameras in conventional passenger elevators was presented in a paper by [4]. The study is limited to the people-counting problem and is not intended to address elevator control optimization.…”

Section: Introductionmentioning

confidence: 99%

Camera-Driven Probabilistic Algorithm for Multi-Elevator Systems

2020

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A fast and reliable vertical transportation system is an important component of modern office buildings. Optimization of elevator control strategies can be easily done using the state-of-the-art artificial intelligence (AI) algorithms. This study presents a novel method for optimal dispatching of conventional passenger elevators using the information obtained by surveillance cameras. It is assumed that a real-time video is processed by an image processing system that determines the number of passengers and items waiting for an elevator car in hallways and riding the lifts. It is supposed that these numbers are also associated with a given uncertainly probability. The efficiency of our novel elevator control algorithm is achieved not only by the probabilistic utilization of the number of people and/or items waiting but also from the demand to exhaustively serve a crowded floor, directing to it as many elevators as there are available and filling them up to the maximum allowed weight. The proposed algorithm takes into account the uncertainty that can take place due to inaccuracy of the image processing system, introducing the concept of effective number of people and items using Bayesian networks. The aim is to reduce the waiting time. According to the simulation results, the implementation of the proposed algorithm resulted in reduction of the passenger journey time. The proposed approach was tested on a 10-storey office building with five elevator cars and traffic size and intensity varying from 10 to 300 and 0.01 to 3, respectively. The results showed that, for the interfloor traffic conditions, the average travel time for scenarios with varying traffic size and intensity improved by 39.94% and 19.53%, respectively.

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Smart Building’s Elevator with Intelligent Control Algorithm based on Bayesian Networks

Cited by 17 publications

References 31 publications

Investigation of Optimization Techniques on the Elevator Dispatching Problem

Investigation of Optimization Techniques on the Elevator Dispatching Problem

Probabilistic estimation of spinning reserves in smart grids with Bayesian-driven reserve allocation adjustment algorithm

Camera-Driven Probabilistic Algorithm for Multi-Elevator Systems

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