Y. Cheng, X. Wang and Y. Zhang, “A Bayesian Reinforcement Learning Algorithm Based on Abstract States for Elevator Group Scheduling Systems,” Chinese Journal of Electronics, Vol. 19, No. 3, 2010, pp. 394-398.

has been cited by the following article:

• TITLE: Intelligent Decisions Modeling for Energy Saving in Lifts:An Application for Kleemann Hellas Elevators

  AUTHORS: Vasilios Zarikas, Nick Papanikolaou, Michalis Loupis, Nick Spyropoulos

  KEYWORDS: Elevators; Energy Consumption Reduction; Energy Engineering; Applied Bayesian Networks; Applied Decision Networks; Applied Influence Diagrams; Applied Intelligent Decisions; Fuzzy Rules

  JOURNAL NAME: Energy and Power Engineering, Vol.5 No.3, May 9, 2013

  ABSTRACT: The present work proposes a methodological approach for modeling decisions regarding energy reduction in an elevator. This is achieved with the integration of existing as well as acquired knowledge, in a decision module implemented in the electronics of an elevator. So far, elevators do not exploit information regarding their recent usage. In the developed system decisions are driven based on information arising from monitoring the use of the elevator. Monitoring provides various records of usage which consequently are used to predict elevator’s future usage and to adapt accordingly its functioning. Till now, there are only elevators that encompass in their electronics algorithms with if then rules in order to control elevator’s functioning. However, these if then rules are based only on good practice knowledge of similar elevators installed in similar buildings. Even this knowledge which unavoidably is associated with uncertainty is not encoded in a mathematically consisted way in the algorithms. The design, the implementation and a first pilot evaluation study of an elevator’s intelligent decision module are presented. The study concludes that the presented application sufficiently reduces energy consumption through properly controlled functioning.