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The CO2 emissions and energy use of SMEs in the tertiary sector (e.g. small food and non-food shops, restaurants, offices, pubs, etc.) are high and there are few initiatives to reduce because this target group is difficult to reach due to small scale and diversity. The Flemish-Dutch TERTS project wants (1) to make the sector aware of the potential of and (2) to demonstrate energy transition and energy efficiency of innovative technologies. This paper is focussing on butcher’s shops. A reference model is made based on data of 90 existing shops in Flanders (Belgium). The energy use of the building and systems is calculated according to DIN V 15 899. The cost-benefit of various measures is calculated and compared. Results show that the main energy consumers of a butcher shop are cooling, lighting and domestic hot water, whereas heating only has a rather small contribution. There are several cooling needs: product-cooling (in walk-in freezers, walk-in coolers and the cooling counter) and cooling of the workshop. The combination of the following measures is concluded to be the most favourable and leads to a reduction in final energy consumption of 60 %: a reflective coating on the flat roof and extra roof insulation, relighting with LED, air-to-water heat pump for the generation of domestic hot water and PV panels as local energy generation.

Throughout their lifetime, buildings might face unpredictable shocks leading to fast deterioration of comfort levels. The ability of buildings and systems to absorb the shock and bring back the indoor conditions to their designed state is termed as “resilience”. Ventilation and thermal resilience have been studied under homogeneous conditions. However, the established airflow indoors and hence resilience is non-homogeneous. In this work, the spatial aspect of ventilation and thermal resilience will be assessed in a classroom equipped with displacement ventilation using 3D CFD modeling. Two sources of pollution were considered in the space: CO2 and VOCs. To study resilience, the numerical model was simulated until steady state. Subsequently, a power outage shock of 60 min was induced. The temporal and spatial mappings of temperature, and pollutants’ concentration were recorded in the occupied zone at the breathing height of 1.2 m and compared to that at the exhaust. Building resilience was assessed through ppm.hours and degree.hours and compared at both locations. Results showed that resilience is rather a non-homogeneous field that depends on the location of heat sources and pollution sources in the space. However, results showed that any over or under estimations (~20 − 28%) in assessing the thermal or ventilation resilience are negligible when evaluated at either the breathing plane or the exhaust.

In school and office buildings the ventilation system has a large contribution to the total energy use. A control strategy that adjusts the operation to the actual demand can significantly reduce the energy use. This is important in rooms with a highly fluctuating occupancy profile. However, a standard rule-based control is reactive, making the installation ‘lag behind’ in relation to the demand. A model predictive control (MPC) might be a solution. To implement an MPC control first a suitable model must be identified for reliable predictions of room temperature and CO2 concentration. For CO2 predictions three scenarios are proposed respectively counting camera, lecture schedule and motion sensor. Two model identification techniques are evaluated: ARX and RC models. For identifying the heating dynamics of the case study building the 3 state RC model showed a good performance, a 5 step ahead prediction on a 15 minute time interval indicated a RMSE of approximately 0.60 °C. The 3rd order ARX model indicated similar results, however the cross validation demonstrated that the RC model outperforms the ARX model. For CO2 predictions the counting scenario resulted in the most accurate n-step ahead predictions. The RMSE found for the RC model is at maximum 90 ppm while 140 ppm for the ARX model. RC models are recommended for modelling all-air HVAC systems attributed by the higher prediction accuracy over ARX models. In addition, these models still contain physical parameters compared to ARX models.

Unlike other types of buildings, commercial and industrial buildings have been so far “forgotten”. In addition, EPBD requirements are increasingly challenging for this type of buildings. This paper aims to identify the most building determinants of the energy performance of commercial and industrial buildings, focussing on the building envelope. Building energy simulations (BES) in TRNSYS are used to simulate the energy demand for heating and cooling in five building variants. The Pareto optimality approach that considers the economic and energetic objectives equally, is used to determine the cost-optimal solutions. The sensitivity analysis and cost-optimal study clearly reveal that airtightness seems to be the most important factor. Although heat recovery on a balanced mechanical ventilation system has a major impact on the energy demand for heating, this measure is not cost-optimal. The large impact of the U-value of the roof on the energy demand for heating is also reflected in the cost-optimal study. The insulation of the floor do not appear to be cost-optimal. Moreover, attention to construction detailing is important. The additional energy losses that can occur due to thermal bridges quickly reach significant values although solving the thermal bridges seems not to be cost-optimal.