Prediction of elevator use. Thanks to the predictive capacity of artificial intelligence algorithms, we optimise the energy management of lifts, as well as reducing the waiting time of users.
It was a research project and they were looking for a company that could give predictive capabilities to the lift to anticipate what the user will do.
Other variables were also considered, such as, in the case of hotels, the nationality of the residents on the floors, meal times, departure and pre-arrival times, alarms requested from reception, etc.
Based on this data, the lift’s operating history is analysed and patterns are established. This predicts the probability of being called to a particular plant at a given time. This allows the lift to travel autonomously to that floor at a very low motor speed, reducing energy consumption.
This improves the experience for users, who have the lift on their floor just when they need it, and improves energy consumption, as the motor runs at the lowest possible speed.