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

Farzad Dadras Javan, Italo Aldo Campodonico Avendano, Behzad Najafi (), Amin Moazami and Fabio Rinaldi
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Farzad Dadras Javan: Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
Italo Aldo Campodonico Avendano: Department of Ocean Operations and Civil Engineering, Faculty of Engineering, NTNU, 6009 Ålesund, Norway
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
Fabio Rinaldi: Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy

Energies, 2023, vol. 16, issue 14, 1-15

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.

Keywords: load forecasting; warehouse buildings; machine learning; flexibility in buildings; demand response; multi-layer perceptron (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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