A machine learning approach to modelling escalator demand response

Uimonen, Semen; Tukia, Toni; Ekström, Jussi; Siikonen, Marja-Liisa; Lehtonen, Matti

doi:10.1016/j.engappai.2020.103521

Cited by 11 publications

(5 citation statements)

References 26 publications

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“…Uimonen et al offered a solution to the problem of selecting acceptable escalators from a vast pool in order to meet the aim of power curtailment at a low cost, and they highlighted the escalator attributes that make the best DR candidates [26]. They examine four different calculating methods that differ in computation speed and accuracy.…”

Section: Based Dr Managementmentioning

confidence: 99%

Artificial Intelligence-Based Demand-Side Response Management of Renewable Energy

Hanna

WANG

et al. 2022

WIT Transactions on Ecology and the Environment

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Renewable energy (RE) sources will aid in the decarbonization of the energy sector, which would assist in alleviating the negative consequences of climate change. However, using RE resources for hybrid power generation has two technological challenges, uncertainty and variability owing to RE features, making estimating generated power availability difficult. Artificial intelligence techniques have been used in a variety of applications in power systems, but demand-side response (DR) is just lately receiving major research interest. The DR is highlighted as one of the most promising ways of providing the electricity system with demand flexibility; as a result, many system operators believe that growing the scale and breadth of the DR programme is critical. There are many different sorts of demand reduction programmes, and the most common classification is dependent on who begins the demand reduction. There are two types of DR schemes: (1) price-based programmes and (2) incentive-based programmes.

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Section: Based Dr Managementmentioning

confidence: 99%

Artificial Intelligence-Based Demand-Side Response Management of Renewable Energy

Hanna

WANG

et al. 2022

WIT Transactions on Ecology and the Environment

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“…The angle of inclination of the escalator is 30 degrees. On the basis of general specifications, 34 the escalator moves at constant speed 0.5 m/s. Table 1.…”

Section: Model Geometry and Simulation Conditionsmentioning

confidence: 99%

“…The angle of inclination of the escalator is 30 degrees. On the basis of general specifications, 34 the escalator moves at constant speed 0.5 m/s. Figure 1 shows the escalator model geometry: (a) the vertical cross‐section dimensions of the escalator corridor are 4.0 m × 4.0 m (W × H) at the tip of steps and the step width of the escalator is 1.0 m; (b) the model of an adult passenger with a height of 1.75 m and a shoulder‐width of 0.5 m; (c) handrails are 1.0 m high, 0.1 m wide, and 0.05 m thick.…”

Section: Numerical Approachmentioning

confidence: 99%

Numerical model for cough‐generated droplet dispersion on moving escalator with multiple passengers

et al. 2022

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The pandemic caused by the novel coronavirus COVID-19 is currently ongoing throughout the world, and careful measures are still required to curb significant loss of life and unprecedented economic loss. Fluid droplets expelled from infected individuals by respiratory events have attracted much attention in research because droplet transmission is considered to be one of the most critical mechanisms responsible for the rapid spread and continued circulation of infection among humans. 1 According to a recent study, airborne transmission is the main route for the spread of COVID-19. 2 Multiple studies on saliva droplets have demonstrated that thousands of virus-laden

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“…It can continuously transport passengers. At present, escalators are widely used and have become an indispensable part of public transportation, and escalators are widely used in crowded locations, especially shopping malls, stations, and subways [1,2]. With the increase in the number of escalators, the safety problems of escalators have become increasingly prominent.…”

Section: Introductionmentioning

confidence: 99%

A Fast Diagnosis Method of Escalator Reversal Faults Based on Dynamic Information and Multiattribute Decision-Making

Jia

Pan

et al. 2021

Journal of Mathematics

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There are many reasons for escalator reversal failure, and the reasons are distributed in different locations. It is difficult to locate the specific location of the fault in the actual fault troubleshooting. At the same time, the information related to the failure is not used in the troubleshooting, so there is a problem of inefficient troubleshooting. To this end, this paper proposes a multiattribute decision-making method that integrates dynamic information and gives the optimal troubleshooting order to improve the efficiency of the troubleshooting. First of all, according to the structure of the escalator components, the escalator reversal fault tree is established. Secondly, a static decision matrix is established by comprehensively considering the failure probability, search cost, and influence degree of the bottom event of the fault tree. Finally, the influence matrix of information on each attribute is given by the dynamic information obtained in troubleshooting, the static decision fusion influence matrix determines the dynamic decision matrix, the dynamic decision matrix is weighted and normalized, and the Technique for Order Preference by Similarity to Ideal Solution is used to determine the optimal troubleshooting order. Taking the reversal failure of a certain type of escalators as an example, the method of multiattribute decision-making of fusion dynamic information is used to shorten the troubleshooting time, improve the efficiency of troubleshooting, and verify the effectiveness of this method.

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A machine learning approach to modelling escalator demand response

Cited by 11 publications

References 26 publications

Artificial Intelligence-Based Demand-Side Response Management of Renewable Energy

Artificial Intelligence-Based Demand-Side Response Management of Renewable Energy

Numerical model for cough‐generated droplet dispersion on moving escalator with multiple passengers

A Fast Diagnosis Method of Escalator Reversal Faults Based on Dynamic Information and Multiattribute Decision-Making

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