Optimal Elevators Control for Buildings Based on Associative Memory

Conventional elevators encounter the issues of energy efficiency, waiting periods, and lack of adaptation under dynamic conditions of the buildings. To this purpose, this paper provides an elevator control optimization model based on associative memory in order to tackle these challenges. To minimize energy usage and waiting time and increase the responsiveness of the entire system to real-time passenger demand. This technology leverages associative memory—specifically Hopfield networks—to effectively learn and reallocate elevator assignments adjusted to past traffic flows. The observed results represent up to 31% energy consumption reductions and 33% waiting time savings that show the function of the proposed system in optimizing traffic and energy consumption patterns at different building sizes and traffic scenarios. This research introduces a new elevator control system employing associative memory, which demonstrates considerable improvement in energy efficiency and lower waiting times for passengers. These findings will lead to additional exploration, notably for real-time application, interaction with IoT and building management systems, and scaling to bigger structures.