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Modeling the aggregated power consumption of elevators – the New York city case study

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  • Tukia, Toni
  • Uimonen, Semen
  • Siikonen, Marja-Liisa
  • Donghi, Claudio
  • Lehtonen, Matti

Abstract

This paper proposes a bottom-up framework for modeling the aggregated power consumption of a fleet of elevators. The paper has two aims: enhancing the research related to the power efficiency of elevators and providing modeling methods and analytical concepts for load modeling of elevators from the perspective of power systems and urban energy systems. As a case study, the paper simulates the total aggregated power consumption profile of elevators in New York City during a weekday and weekend day. Furthermore, the paper provides methods for expanding the analysis to other regions and cities which lack detailed background data of elevator installations. The results imply that elevators consume more than 1% of the annual electrical energy in the city, while the hourly ratio has more variation, typically between 0.5% and 3% of the total power demand. Additionally, the quantity of elevators required to be modeled or measured from a random set to attain credible predictions of the total aggregated power consumption of the elevator population depends on the applied time resolution.

Suggested Citation

  • Tukia, Toni & Uimonen, Semen & Siikonen, Marja-Liisa & Donghi, Claudio & Lehtonen, Matti, 2019. "Modeling the aggregated power consumption of elevators – the New York city case study," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:251:y:2019:i:c:26
    DOI: 10.1016/j.apenergy.2019.113356
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    References listed on IDEAS

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    1. Fan, Guo-Feng & Peng, Li-Ling & Hong, Wei-Chiang, 2018. "Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model," Applied Energy, Elsevier, vol. 224(C), pages 13-33.
    2. Christine H Pout, 2000. "N-DEEM: The National Nondomestic Buildings Energy and Emissions Model," Environment and Planning B, , vol. 27(5), pages 721-732, October.
    3. Xiaodong Zhang & Xiaoli Li & Kang Wang & Yanjun Lu, 2014. "A Survey of Modelling and Identification of Quadrotor Robot," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-16, October.
    4. Yeo, In-Ae & Yoon, Seong-Hwan & Yee, Jurng-Jae, 2013. "Development of an urban energy demand forecasting system to support environmentally friendly urban planning," Applied Energy, Elsevier, vol. 110(C), pages 304-317.
    5. Morvaj, Boran & Evins, Ralph & Carmeliet, Jan, 2017. "Decarbonizing the electricity grid: The impact on urban energy systems, distribution grids and district heating potential," Applied Energy, Elsevier, vol. 191(C), pages 125-140.
    6. Keirstead, James & Jennings, Mark & Sivakumar, Aruna, 2012. "A review of urban energy system models: Approaches, challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3847-3866.
    7. Feng, Yonghan & Ryan, Sarah M., 2016. "Day-ahead hourly electricity load modeling by functional regression," Applied Energy, Elsevier, vol. 170(C), pages 455-465.
    8. Chen, Yixing & Hong, Tianzhen & Piette, Mary Ann, 2017. "Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis," Applied Energy, Elsevier, vol. 205(C), pages 323-335.
    9. Alhamwi, Alaa & Medjroubi, Wided & Vogt, Thomas & Agert, Carsten, 2018. "Modelling urban energy requirements using open source data and models," Applied Energy, Elsevier, vol. 231(C), pages 1100-1108.
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    Cited by:

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    2. Jia Hui Ang & Yusri Yusup & Sheikh Ahmad Zaki & Ali Salehabadi & Mardiana Idayu Ahmad, 2022. "Comprehensive Energy Consumption of Elevator Systems Based on Hybrid Approach of Measurement and Calculation in Low- and High-Rise Buildings of Tropical Climate towards Energy Efficiency," Sustainability, MDPI, vol. 14(8), pages 1-21, April.
    3. Yanfang Dong & Caihang Liang & Lili Guo & Xiaoliang Cai & Weipeng Hu, 2023. "Life Cycle Carbon Dioxide Emissions and Sensitivity Analysis of Elevators," Sustainability, MDPI, vol. 15(17), pages 1-23, August.
    4. Hunt, Julian David & Nascimento, Andreas & Zakeri, Behnam & Jurasz, Jakub & Dąbek, Paweł B. & Barbosa, Paulo Sergio Franco & Brandão, Roberto & de Castro, Nivalde José & Leal Filho, Walter & Riahi, Ke, 2022. "Lift Energy Storage Technology: A solution for decentralized urban energy storage," Energy, Elsevier, vol. 254(PA).

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