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


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.

Share and Cite:

V. Zarikas, N. Papanikolaou, M. Loupis and N. Spyropoulos, "Intelligent Decisions Modeling for Energy Saving in Lifts:An Application for Kleemann Hellas Elevators," Energy and Power Engineering, Vol. 5 No. 3, 2013, pp. 236-244. doi: 10.4236/epe.2013.53023.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] L. Cui, “An Elevator Intelligent Scheduling Method Using Neural Network Control,” International Journal of Digital Content Technology and Its Applications, Vol. 7, No. 3, 2013, pp. 174-181.
[2] T. Chen, Y.-Y. Hsu and Y.-J. Huang, “Optimizing the Intelligent Elevator Group Control System by Using Genetic Algorithm,” Advanced Science Letters, Vol. 9, No. 1, 2012, pp. 957-962. doi:10.1166/asl.2012.2654
[3] P. E. Utgoff and M. E. Connell, “Real-Time Combinatorial Optimization for Elevator Group Dispatching,” IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, Vol. 42, No. 1, 2012, pp. 130-146.
[4] N. A. Rahim, H. W. Ping and J. Jamaludin, “A Novel Self-Tuning Scheme for Fuzzy Logic Elevator Group Controller,” IEICE Electronics Express, Vol. 7, No. 13, 2010, pp. 892-898.
[5] 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.
[6] X.-C. Wang and D.-M. Yang, “Intelligent Algorithm of Elevator Group Control by Statistic Approximation,” Xi tong Fangzhen Xuebao/Journal of System Simulation, Vol. 13, 2001, pp. 100-101.
[7] F. V. Jensen, “An Introduction to Bayesian Networks,” UCL Press Limited, London, 2000.
[8] J. Pearl, “Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference,” Morgan Kaufmann, San Mateo, 1988.
[9] J. Stutz and P. Cheeseman, “A Short Exposition on Bayesian Inference and Probability,” National Aeronautic and Space Administration Ames Research Centre: Com putational Sciences Division, Data Learning Group, 1994.
[10] N. Friedman and M. Goldszmidt, “Learning Bayesian Network from Data,” SRI International, Menlo Park, 1998.
[11] D. Heckerman and D. Geiger, “Learning Bayesian Net works,” Microsoft Research, Redmond, 1994, p. 3.
[12] J. Pearl, “Fusion, Propagation and Structuring in Belief Networks,” Artificial Intelligence, Vol. 29, No. 3, 1986, pp. 241-288. doi:10.1016/0004-3702(86)90072-X
[13] J. Pearl, “Evidential Reasoning Using Stochastic Simulation of Causal Models,” Artificial Intelligence, Vol. 32, No. 2, 1987, pp. 245-258. doi:10.1016/0004-3702(87)90012-9
[14] J. Pearl and T. Verma, “The Logic of Representing De pendencies by Directed Graphs,” Proceedings, AAAI Conference, Seattle, 13-17 July 1987, pp. 374-379.
[15] R. L. Winkler, “An Introduction to Bayesian Inference and Decision,” Holt, Rinehart and Winston, Toronto, 1972.
[16] E. J. Horvitz, J. S. Breese and M. Henrion, “Decision Theory in Expert Systems and Artificial Intelligence,” International Journal of Approximate Reasoning, Vol. 2, No. 3, 1988, pp. 247-302.
[17] B. W. Morgan, “An Introduction to Bayesian Statistical Decision Processes,” Prentice-Hall Inc., Englewood Cliffs, 1968, p. 15.
[18] J. Pearl, “Influence Diagrams–Historical and Personal Perspectives,” Decision Analysis, Vol. 2, No. 4, 2005, pp. 232-234. doi:10.1287/deca.1050.0055
[19] L. A. Zadeh, “The Concept of a Linguistic Variable and Its Application to Approximate Reasoning,” Information Science, Vol. 8, No. 3, 1975, pp. 199-249. doi:10.1016/0020-0255(75)90036-5
[20] H. M. Saraoglu and S. Sanli, “A Fuzzy Logic-Based Decision Support System on Anesthetic Depth Control for Helping Anesthetists in Surgeries,” Journal of Medical Systems, Vol. 31, No. 6, 2007, pp. 511-519.
[21] VDI 4707 Guideline, “Lifts Energy Efficiency,” 2008.
[22] G. Barney, “Vertical Transportation in Tall Buildings,” Elevator World, Vol. LI, No. 5, 2003, pp. 66-75.
[23] CIBSE, “Guide D Transportation Systems in Buildings,” 2005.
[24] J. Nipkow, “Electricity Consumption and Efficiency Po tentials of Lifts,” Report of Swiss Agency for Efficient Energy Use SAFE, HTW Chur University of Applied Sciences, Zurich, 2005.
[25] N. Spyropoulos and L. Asvestopoulos, “Hydraulic vs. Traction Lifts: Environment Friendliness and Quality of Service to the User,” The 17th International Congress on Vertical Transportation Technologies, Thessaloniki, 11 13 June 2008, pp. 247-251.
[26] N. Mutoh, Y. Hayano, H. Yahagi and K. Takita, “Electric Braking Control Methods for Electric Vehicles with In dependently Driven Front and Rear Wheels,” IEEE Tran sactions on Industrial Electronics, Vol. 54, No. 2, 2007, pp. 1168-1176. doi:10.1109/TIE.2007.892731
[27] M.-J. Yang, H.-L. Jhou, B.-Y. Ma and K.-K. Shyu, “A Cost Effective Method of Electric Brake with Energy-Regeneration for Electric Vehicles,” IEEE Transactions on Industrial Electronics, Vol. 56, No. 6, 2009, pp. 2203-2212. doi:10.1109/TIE.2009.2015356

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.