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- #Elevator traffic analysis design and control full
- #Elevator traffic analysis design and control software
Under the conditions above, when a hall call is registered at the 6th floor to go to the 1st floor, Through a maximum 10% reduction in energy consumption compared to our conventional system, this system allows building owners to cut energy costs without sacrificing passenger convenience.Ĭar B: About to leave the 9th floor with several passengers Accordingly, if multiple cars have the same traveling distance, this system chooses the car that requires the least energy. A car uses energy efficiently when it travels down with a heavy load, or up with a light load. Priority is given to operational efficiency during peak hours and energy efficiency during non-peak hours.Ĭar allocation that maximizes operational efficiency does not necessarily translate to energy efficiency.
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This system selects the elevator in a group that best balances operational efficiency and energy consumption. Maximizing operational efficiency and minimizing energy consumption All cars work cooperatively for optimum operation. Through evaluation of the registered hall call and the forecasted call, the best car is assigned. When a hall call is registered, the algorithm predicts a near-future call that could require long waits. The possibility of long waits caused by future callsįorecasting a near-future hall call to reduce long waits.
#Elevator traffic analysis design and control full
The possibility of a car being bypassed due to a full load.Your waiting time or an increase in waiting time throughout the building.Selection is based on each elevator's potential energy consumption according to its current location and passenger load. With Mitsubishi Electric's smart control technology, when a passenger presses a hall call button, the system selects the elevator that best balances operational efficiency and energy consumption. The space saved can be used effectively for other building facilities. Compared to the conventional control system, DOAS allows reduction in car size and hoisting area. The longer passengers wait for a car, the more they become irritated.ĭestination Oriented Allocation System (DOAS) increases handling capacity. ΣAI-2200C reduces not only passengers' waiting time but also long-wait. The optimal car allocation based on the evaluation minimizes irritation of all passengers. This group control system evaluates not only actual waiting time but also psychological waiting time by assessing the probability of full-load bypass and prediction error, etc. This group control system has a function to allocate the cars flexibly in response to the traffic conditions by sending the cars to a congested floor during periods of heavy traffic. Traffic conditions in a building change constantly. The lift traffic analysis have been carried out by examining the simulation results obtained.In addition to reduction of waiting time, this group control system reduces travel time (from boarding a car to arriving at a destination floor) by individualized car allocation.
#Elevator traffic analysis design and control software
In this paper the neural network approach has been applied to Duplex/Triplex group control systems for improving passenger waiting time and a lift simulation software has been developed and implemented in order to assess the learning capability by measuring the performance of the control algorithm. Neural networks can dynamically learn the behavior of an elevator system and predict the next floors to stop, based on what has been learnt. Elevator control algorithms utilizing neural networks aims at distributing the most suitable cars to the floors by considering the passenger service demand. In particular, neural networks can offer better solutions to the passenger call allocation process when compared to the classical traffic control methods. They have also been applied to basic problems in elevator traffic control systems, such as the prediction and control of elevator movements. Artificial intelligence methods employing neural networks have been proved to be successful in many fields, such as process modeling, pattern recognition and classification problems. Elevator traffic control systems have become more and more complicated due to their nature of intelligence.