Affiliations: [a] School of Electrical and Computer Engineering, Seoul National University, Seoul 151-742, Korea | [b] Department of Computer Engineering, Ewha University, Seoul 120-750, Korea
Correspondence:
[*]
Corresponding author: Hyokyung Bahn, Department of Computer Engineering, Ewha University, Seoul 120-750, Korea. E-mail:[email protected]
Abstract: With the recent advances in energy-aware building
technologies, the electricity usage of a smart building is detected every
moment and might have different costs at each time slot of a day. This
article presents a new elevator scheduling algorithm for a smart building
that considers the dynamic changes of electricity price and passenger
traffic. The goal of our algorithm is to minimize the electricity charge
without increasing passengers' waiting time. To this end, we use a control
parameter to increase the number of working elevator cars when the passenger
traffic is heavy or the electricity price becomes low. In contrast, when the
electricity price becomes high (i.e., peak time), the system adjusts the
control parameter to reduce the number of working elevator cars. This is not
a simple issue as the two goals we pursue sometimes conflict. Thus, we use
an optimization technique based on genetic algorithms in the design of our
scheduler. To evaluate the proposed elevator scheduling system, we conduct
experiments under synthetic and realistic workload conditions. The results
show that the proposed elevator scheduling system significantly saves the
electricity charge of the conventional elevator scheduling system.
Specifically, the average reduction in the electricity charge is 68.3%
without sacrificing passengers' waiting time.
Keywords: Elevator scheduling, group elevator, electricity price, smart building, genetic algorithm
