IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i17p4401-d1470159.html
   My bibliography  Save this article

Just-in-Time Morning Ramp-Up Implementation in Warehouses Enabled by Machine Learning-Based Predictive Modelling: Estimation of Achievable Energy Saving through Simulation

Author

Listed:
  • Ali Kaboli

    (Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy)

  • Farzad Dadras Javan

    (Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy)

  • Italo Aldo Campodonico Avendano

    (Department of Ocean Operations and Civil Engineering, Faculty of Engineering, Norwegian University of Science and Technology (NTNU), 6009 Ålesund, Norway)

  • Behzad Najafi

    (Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy)

  • Luigi Pietro Maria Colombo

    (Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy)

  • Sara Perotti

    (Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Via Lambruschini 4/B, 20156 Milan, Italy)

  • Fabio Rinaldi

    (Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy)

Abstract

This study proposes a simulation-based methodology for estimating the energy saving achievable through the implementation of a just-in-time morning ramp-up procedure in a warehouse (equipped with a heat pump). In this methodology, the operation of the heating supply unit each day is initiated at a different time, aiming at achieving the desired setpoint upon (and not before) the expected arrival of the occupants. It requires the estimation of the ramp-up duration (the time it takes the heating system to bring the indoor temperature to the desired setpoint), which can be provided by machine learning-based models. To justify the corresponding required deployment investment, an accurate estimation of the resulting achievable energy saving is needed. Accordingly, physics-based energy behavior simulations are first performed. Next, various ML algorithms are employed to estimate the ramp-up duration using the simulated time-series data of indoor temperature, setpoints, and weather conditions. It is shown that the proposed pipelines can estimate the ramp-up duration with a mean absolute error of about 3 min in all indoor spaces. To assess the resulting potential energy saving, a re-simulation is conducted using ML-based ramp-up estimations for each day, resulting in an energy savings of approximately 10%.

Suggested Citation

  • Ali Kaboli & Farzad Dadras Javan & Italo Aldo Campodonico Avendano & Behzad Najafi & Luigi Pietro Maria Colombo & Sara Perotti & Fabio Rinaldi, 2024. "Just-in-Time Morning Ramp-Up Implementation in Warehouses Enabled by Machine Learning-Based Predictive Modelling: Estimation of Achievable Energy Saving through Simulation," Energies, MDPI, vol. 17(17), pages 1-18, September.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4401-:d:1470159
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/17/4401/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/17/4401/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Farzad Dadras Javan & Italo Aldo Campodonico Avendano & Behzad Najafi & Amin Moazami & Fabio Rinaldi, 2023. "Machine-Learning-Based Prediction of HVAC-Driven Load Flexibility in Warehouses," Energies, MDPI, vol. 16(14), pages 1-15, July.
    2. Nweye, Kingsley & Nagy, Zoltan, 2022. "MARTINI: Smart meter driven estimation of HVAC schedules and energy savings based on Wi-Fi sensing and clustering," Applied Energy, Elsevier, vol. 316(C).
    3. Amasyali, Kadir & El-Gohary, Nora M., 2021. "Real data-driven occupant-behavior optimization for reduced energy consumption and improved comfort," Applied Energy, Elsevier, vol. 302(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    2. Łukasz Amanowicz, 2021. "Peak Power of Heat Source for Domestic Hot Water Preparation (DHW) for Residential Estate in Poland as a Representative Case Study for the Climate of Central Europe," Energies, MDPI, vol. 14(23), pages 1-15, December.
    3. Barbara Widera, 2024. "Energy and Carbon Savings in European Households Resulting from Behavioral Changes," Energies, MDPI, vol. 17(16), pages 1-36, August.
    4. Du, Feng & Yue, Hong & Zhang, Jiangfeng, 2023. "Influence of advertisement control to residential energy savings in large networks," Applied Energy, Elsevier, vol. 333(C).
    5. Gao, Datong & Kwan, Trevor Hocksun & Hu, Maobin & Pei, Gang, 2022. "The energy, exergy, and techno-economic analysis of a solar seasonal residual energy utilization system," Energy, Elsevier, vol. 248(C).
    6. Zhou, Ying & Wang, Yu & Li, Chenshuang & Ding, Lieyun & Yang, Zhigang, 2024. "Energy-efficiency oriented occupancy space optimization in buildings: A data-driven approach based on multi-sensor fusion considering behavior-environment integration," Energy, Elsevier, vol. 299(C).
    7. Martin Stöckl & Johannes Idda & Volker Selleneit & Uwe Holzhammer, 2023. "Flexible Operation to Reduce Greenhouse Gas Emissions along the Cold Chain for Chilling, Storage, and Transportation—A Case Study for Dairy Products," Sustainability, MDPI, vol. 15(21), pages 1-27, November.
    8. Brudermueller, Tobias & Kreft, Markus & Fleisch, Elgar & Staake, Thorsten, 2023. "Large-scale monitoring of residential heat pump cycling using smart meter data," Applied Energy, Elsevier, vol. 350(C).
    9. Dharmesh Dhabliya & Rajasoundaran Soundararajan & Parthiban Selvarasu & Maruthi Shankar Balasubramaniam & Anand Singh Rajawat & S. B. Goyal & Maria Simona Raboaca & Traian Candin Mihaltan & Chaman Ver, 2022. "Energy-Efficient Network Protocols and Resilient Data Transmission Schemes for Wireless Sensor Networks—An Experimental Survey," Energies, MDPI, vol. 15(23), pages 1-33, November.
    10. Xu, Xiaoxiao & Yu, Hao & Sun, Qiuwen & Tam, Vivian W.Y., 2023. "A critical review of occupant energy consumption behavior in buildings: How we got here, where we are, and where we are headed," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    11. Luis Gomes & António Coelho & Zita Vale, 2022. "Assessment of Energy Customer Perception, Willingness, and Acceptance to Participate in Smart Grids—A Portuguese Survey," Energies, MDPI, vol. 16(1), pages 1-16, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4401-:d:1470159. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.