"Artificial intelligence", based learning system predicting "pea

Elevator – industrial lift truck – or stationary lift for vehicle – Stationary lift for roadway vehicle or required component... – Having specific drive means for support

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Details

187128, B66B 120

Patent

active

052411427

ABSTRACT:
The present invention is directed to an elevator dispatching system for controlling the assignment of elevator cars. More particularly, the present invention is directed to a method of determining the commencement and/or conclusion of UP-PEAK and DOWN-PEAK periods of operation. For example, for commencing UP-PEAK operation, a lobby boarding rate is predicted, based on historical information of the number of passengers boarding the elevators at the lobby and the number elevators leaving the lobby. The predicted lobby boarding rate is compared with a predetermined threshold value. If the predicted lobby boarding rate is greater than the predetermined threshold value, UP-PEAK is commenced. In the preferred embodiment, the predetermined threshold value is a predetermined percentage of the elevator car's capacity. Additionally, the present invention is directed to a method of adjusting the threshold value based on actual passenger traffic. For example, once UP-PEAK is commenced, the load of the first few elevators leaving the lobby within a predetermined time interval is determined, and the threshold value is adjusted based on their determined load. If the determined load is greater than a certain percentage of the elevator car's capacity, indicative of starting UP-PEAK too late, the threshold value is decreased. Similarly, if the determined load is less than a certain percentage of the elevator car's capacity, indicative of starting UP-PEAK too soon, the threshold value is increased.

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Forecasting Methods and Applications, Spyros Makridakis & Steven C. Wheelwright (John Wiley & Sons, Inc.; 1978) Section 3.3: Single Exponential Smoothing and Section 3.6; "Linear Exponential Smoothing".
Intelligent Elevator Dispatching System by N. Kameli & K. Thangavelu AI Expert, Sep. 1989, pp. 32-37.

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