A machine learning approach to modelling escalator demand response. (April 2020)

• Record Type: Journal Article
• Authors: Uimonen, Semen; Tukia, Toni; Ekström, Jussi; Siikonen, Marja-Liisa; Lehtonen, Matti
• Is Part Of: Engineering applications of artificial intelligence. Volume 90(2020)
• Publication Date: 2020-04
• Subjects: Escalators -- Vertical transportation -- Demand response -- Modelling -- Random forest -- Neural networks; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.103521
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: This article relates to the topic of the escalator demand response potential. Previous studies mapped escalators as an unrealized potential for additional demand response. The decrease of the nominal speed is the proposed method of reducing the power consumption of an escalator that comes at the cost of passenger travel time and queuing. This work proposes a solution to a problem of selecting appropriate escalators from a large pool to accommodate the target of power curtailment at a minimum cost and highlights the escalator features that constitute the best demand response candidates. The paper compares four methods which differ in calculation speed and accuracy. The primal solution is the earlier developed and enhanced simulation-based model. The random forest and the neural network models provide a solution trained on the output of the simulation-based model aiming to enhance the calculation speed. Finally, all of the developed solutions are compared to the random selection of escalators. The comparison of the proposed statistical approaches shows that the random forest outperforms the neural networks with a maximum error in the prediction of the overall costs in the range of 10.5% of the simulation-based model solution, while the neural network solution lies within 10%–58%, depending on the targeted value of the power reduction. Statistical approaches enable performing predictions for different times of the day and for new escalator populations without the need for time-demanding simulations. Comparison to the random selection of escalators demonstrates that the proposed models generally outperform the random selection at least seven-fold.