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

Ali Kaboli, Farzad Dadras Javan, Italo Aldo Campodonico Avendano, Behzad Najafi (), Luigi Pietro Maria Colombo, Sara Perotti and Fabio Rinaldi
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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

Energies, 2024, vol. 17, issue 17, 1-18

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%.

Keywords: smart building; machine learning; ramp-up duration estimation; warehouse HVAC load; energy optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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