Modelling and real-data validation of a logistic centre using TRNSYS®: Influences of the envelope, infiltrations and stored goods

Abstract

In this paper, the authors aim to support users when modelling scenarios with complex processes entailing thermal loads and infiltrations. The large building analysed is a logistics centre for the replenishment and distribution of perishable foodstuffs where cold chains must be maintained. The logistics centre, with 96 loading/unloading docks, handles large turnovers of different goods. This produces heat inside the facility. Due to continuous loading/unloading, the infiltrations in the building, and the fixed and variable thermal loads, this facility consumes a large amount of energy.Aiming to optimise the centre and contribute to sustainable development goal SDG7, this building has been modelled with a classical non-D envelope using TRNBuild® and also with the more sophisticated 3D software, SketchUp®, to compare and validate their results over a year with real consumption, as well as to assess the main sources of energy consumption. To obtain reliable results, the authors provide some methodology models to identify the sources of the building's thermal losses and quantify the different sources of consumption. These models are useful tools to support decision-makers (to improve insulation and arrange loads, among other things) when trying to reduce energy use in large buildings with intense operating processes.The results indicate that by modelling the entire facility with 3D software, the model estimation differs from real consumption by around 7.22%, while using a non-D model increases the difference to 26%. Additionally, the results show that around 47% of the energy consumption in the building is due to air infiltrations during loading/unloading, 18% is due to perishable products, and around 30% is due to building insulation. The methodology and models presented here, including the possibility of modifying the thermal load profiles, have demonstrated their capacity to reduce and optimise the load demand of refrigeration for warehouses if reliable data records are available.