Abstract
Implementation of the intelligent elevator control systems based on machine-learning algorithms should play an important role in our effort to improve the sustainability and convenience of multi-floor buildings. Traditional elevator control algorithms are not capable of operating efficiently in the presence of uncertainty caused by random flow of people. As opposed to conventional elevator control approach, the proposed algorithm utilizes the information about passenger group sizes and their waiting time, provided by the image acquisition and processing system. Next, this information is used by the probabilistic decision-making model to conduct Bayesian inference and update the variable parameters. The proposed algorithm utilizes the variable elimination technique to reduce the computational complexity associated with calculation of marginal and conditional probabilities, and Expectation-Maximization algorithm to ensure the completeness of the data sets. The proposed algorithm was evaluated by assessing the correspondence level of the resulting decisions with expected ones. Significant improvement in correspondence level was obtained by adjusting the probability distributions of the variables affecting the decision-making process. The aim was to construct a decision engine capable to control the elevators actions, in way that improves user’s satisfaction. Both sensitivity analysis and evaluation study of the implemented model, according to several scenarios, are presented. The overall algorithm proved to exhibit the desired behavior, in 94% case of the scenarios tested.
Highlights
Environmental degradation and depletion of natural resource force us to pursue sustainable and not greedy way of living
The proposed algorithm is applied on top of the collective control strategy, where an elevator control algorithm dispatches an elevator such that it travels in one direction and stops only to pick up people who travel in the same direction
The elevator control algorithm, proposed by this study, sends commands to an elevator system based on the information about the size of the group of people waiting for the elevator
Summary
Environmental degradation and depletion of natural resource force us to pursue sustainable and not greedy way of living. A typical smart building solution enables automated control of building‟s heating, ventilation, airconditioning, lighting, fire alarm, security and elevator systems. The latter attracts particular interest of the research community, since an effective operation of elevator system is a challenging yet rewarding task. The dispatching of the elevator cars, in this case, may be performed with more emphasis on moving people form lobby to their office floors as opposed to inter-floor movements. Another intelligent EGC system, the so-called destination control (DC) system, groups passengers according to their destination. The negative impact of these uncertainties on operational efficiency of the elevator system can be mitigated through utilization of Artificial Intelligence (AI) algorithms
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More From: International Journal of Advanced Computer Science and Applications
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