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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM63880

A Review on Smart Energy Management in Elevator Systems using machine learning

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63880

Volume:

10 |

5 |

May 2026

Pages:

1-5

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Mahalakshmi P G

Mahalakshmi P G

Department of Computer Science and Engineering

Malnad College of Engineering

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SM

Shreyas M

Department of Computer Science and Engineering

Malnad College of Engineering

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KD

Kavana D

Department of Computer Science and Engineering

Malnad College of Engineering

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BKS

Bharath Kumar S

Department of Computer Science and Engineering

Malnad College of Engineering

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Mentors

SP

Sunitha P

Department of Computer Science and Engineering

Malnad College of Engineering

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Abstract

Modern elevator systems face challenges such as long waiting time, unnecessary movement, and high energy consumption. This project proposes EcoLift, an intelligent el- evator dispatching system that uses Genetic Algorithm (GA)- based optimization and sensor-based passenger verification to improve elevator efficiency. The system aims to reduce passen- ger waiting time, optimize journey time, and minimize energy consumption. The proposed solution combines software-based optimization with low-cost hardware components for practical implementation.

KEYWORDS

Index Terms—Elevator Dispatching System Energy Optimiza- tion Genetic Algorithm Artificial Intelligence Smart Elevator Waiting Time Reduction Journey Time Optimization IoT In- telligent Transportation System Energy Efficient System

Cite This Article

Mahalakshmi P G, Shreyas M, Kavana D, Bharath Kumar S, Sunitha P, "A Review on Smart Energy Management in Elevator Systems using machine learning," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-5, May 2026.

Full Article/Conclusion

EcoLift is an intelligent elevator dispatching system de- veloped to improve energy efficiency and passenger comfort using Genetic Algorithm-based optimization and sensor-based passenger verification. The project provides a low-cost and practical solution suitable for smart building applications. Future enhancements may include: •  Machine Learning-based traffic prediction •  Multi-elevator coordination •  IoT cloud monitoring •  Mobile application integration

References

[1]     P. Corte´s, J. Mun˜uzuri, and A. V. Onieva, “Elevator group control as a constrained multiobjective optimization problem,” Applied Soft Computing, vol. 111, 2022. [2]     A. Elhassan, M. Ahmed, and S. Kim, “Connected smart elevator systems for smart power and time saving,” Scientific Reports, vol. 14, no. 1, pp. 1–15, 2024. [3]     H. Zhang and Y. Li, “Direct adaptive neural network-based sliding mode control of a high-speed ultratall building elevator using genetic algorithm,” SN Applied Sciences, vol. 4, no. 3, pp. 1–14, 2022. [4]     J. Lee and K. Park, “Optimization of energy consumption in vertical mobility systems of high-rise office buildings,” Energy Efficiency, vol. 17, no. 2, pp. 1–16, 2024. [5]     S. Doolaard and M. A. Piette, “Explicit data-driven prediction model of annual energy consumed by elevators in residential buildings,” Sustainable Cities and Society, vol. 53, 2020. [6]     A. K. Singh and R. Verma, “Energy savings in elevators by using a particular permanent-magnet motor drive,” Energies, vol. 16, no. 12, pp. 1–18, 2023. [7]     L. Mart´ınez and P. Gonza´lez, “Differential evolution algorithm for optimizing the energy usage of vertical transportation in an elevator,” Sustainability, vol. 14, no. 5, pp. 1–19, 2022. [8]     Y. Wang, Z. Chen, and X. Liu, “Digital twin-based virtual energy storage for elevator car air conditioning,” Proceedings of the Institution of Mechanical Engineers, vol. 240, no. 1, pp. 1–13, 2026. [9]     M. Ibrahim and T. Hassan, “Engineering energy measurement system and data analysing for an electric elevator,” Procedia Computer Science, vol. 231, pp. 250–257, 2024. [10]    R. Silva and J. Moreno, “Exploring heuristic and optimization ap- proaches for elevator group control systems,” Applied Sciences, vol. 14, no. 3, pp. 995–1012, 2024.

Editorial & Review Transparency

Publication Date

5/24/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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