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Machine-Learning-Based Prediction of HVAC-Driven Load Flexibility in Warehouses

Author

Listed:
  • Farzad Dadras Javan

    (Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
    These authors contributed equally to this work.)

  • Italo Aldo Campodonico Avendano

    (Department of Ocean Operations and Civil Engineering, Faculty of Engineering, NTNU, 6009 Ålesund, Norway
    These authors contributed equally to this work.)

  • Behzad Najafi

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

  • Amin Moazami

    (Department of Ocean Operations and Civil Engineering, Faculty of Engineering, NTNU, 6009 Ålesund, Norway
    Department of Architectural Engineering, SINTEF Community, SINTEF AS, Børrestuveien 3, 0373 Oslo, Norway)

  • Fabio Rinaldi

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

Abstract

This paper introduces a methodology for predicting a warehouse’s reduced load while offering flexibility. Physics-based energy simulations are first performed to model flexibility events, which involve adjusting cooling setpoints with controlled temperature increases to reduce the cooling load. The warehouse building encompasses office and storage spaces, and three cooling scenarios are implemented, i.e., exclusive storage area cooling, exclusive office area cooling, and cooling in both spaces, to expand the study’s potential applications. Next, the simulation data are utilized for training machine learning (ML)-based pipelines, predicting five subsequent hourly energy consumption values an hour before the setpoint adjustments, providing time to plan participation in demand response programs or prepare for charging electric vehicles. For each scenario, the performance of an Artificial Neural Network (ANN) and a tree-based ML algorithm are compared. Moreover, an expanding window scheme is utilized, gradually incorporating new data and emulating online learning. The results indicate the superior performance of the tree-based algorithm, with an average error of less than 3.5% across all cases and a maximum hourly error of 7%. The achieved accuracy confirms the method’s reliability even in dynamic scenarios where the integrated load of storage space and offices needs to be predicted.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5407-:d:1195143
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    References listed on IDEAS

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    1. Germán Ramos Ruiz & Carlos Fernández Bandera, 2017. "Validation of Calibrated Energy Models: Common Errors," Energies, MDPI, vol. 10(10), pages 1-19, October.
    2. Manoj Manivannan & Behzad Najafi & Fabio Rinaldi, 2017. "Machine Learning-Based Short-Term Prediction of Air-Conditioning Load through Smart Meter Analytics," Energies, MDPI, vol. 10(11), pages 1-17, November.
    3. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    4. Zixu Liu & Xiaojun Zeng & Fanlin Meng, 2018. "An Integration Mechanism between Demand and Supply Side Management of Electricity Markets," Energies, MDPI, vol. 11(12), pages 1-23, November.
    5. Sha, Huajing & Xu, Peng & Lin, Meishun & Peng, Chen & Dou, Qiang, 2021. "Development of a multi-granularity energy forecasting toolkit for demand response baseline calculation," Applied Energy, Elsevier, vol. 289(C).
    6. Tania Cerquitelli & Giovanni Malnati & Daniele Apiletti, 2019. "Exploiting Scalable Machine-Learning Distributed Frameworks to Forecast Power Consumption of Buildings," Energies, MDPI, vol. 12(15), pages 1-18, July.
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    Cited by:

    1. 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.
    2. 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.

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