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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
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    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).
    4. Piecyk, Maja I. & McKinnon, Alan C., 2010. "Forecasting the carbon footprint of road freight transport in 2020," International Journal of Production Economics, Elsevier, vol. 128(1), pages 31-42, November.
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