Optimization and estimation based ambient intelligence application for control of group elevator systems

In our day, usage of more than one elevator car has become a necessity to ensure the vertical transport service in terms of growing number of high-rise buildings. In these systems, particularly time and energy efficiency is important for elevator users, so the development of high-performance algorithms becomes an unavoidable reality. Although there are several studies in the literature about this subject, because all parameters could not be taken into consideration it may lead to loss of comfort, time and energy in these systems. In this paper, application of optimization and new estimation based ambient intelligence for group elevator control systems were studied. This approach basically aims to balance the average waiting time and average transit time of users and energy consumption values of elevator cars by combining the results of many optimization algorithms and an estimation algorithm. The efficiency obtained by algorithm which is developed based on the characteristics of the different buildings and cars, is confirmed by the results of the simulation of various scenarios. Genetic algorithm, DNA computing and artificial immune system used for optimization and the results obtained by an estimation algorithm were combined by using fuzzy logic. The combinations of 2-5 cars and 10-20 floor buildings in various scenarios were used to verify the proposed new approach, so about 20%-25% time saving and nearly 10% energy savings have been obtained. The important point here, it is aimed to provide the use of current energy equally among all the cars. In this context, energy consumption on an equal basis of cars shows that they use energy at equal level, so it also shows the optimal usage as much as possible in terms of time. Therefore, in this thesis, the efficiency was provided by using many optimization algorithms and the properties obtained by estimation algorithms together.