This study presents a detailed analysis of energy consumption in supermarkets, combining field measurement and parametric analysis to optimize efficiency using theoretical modelling. Using a 6780 m² supermarket near Stockholm as a case study, advanced technologies were integrated, including CO2 refrigeration cycle with heat recovery, ground boreholes as thermal storage, LED lighting, PV panels, and optimized ventilation. Energy data were collected through sub-metering, synchronized with temperature and pressure measurements of the refrigeration cycle, and analysed to validate EnergyPlus/Python simulations. Simulations used for evaluating refrigeration demands, HVAC loads, and overall energy consumption. Results show a 60 % reduction in energy use intensity comparing to a basic-reference supermarket, achieving 140 kWh/m² annually. Key findings include a 17 % reduction in electricity use with LED lighting, a 55 % decrease in medium-temperature (MT) cabinets’ refrigeration demand and a 64 % drop in space heating demand due by adding doors in the MT cabinets. Integrating heat recovery into the CO2 refrigeration system reduces energy consumption by an additional 7 %. Thermal storage further improved system efficiency by optimizing subcooling, while photovoltaic panels reduce grid dependency by 29 %. These results demonstrate the potential of modern technologies to improve energy performance and sustainability in the retail sector, offering actionable insights for designers and policymakers.

Digital Twins are a promising concept to integrate model-based design and operational applications. This study focuses on the control-related performance gap of heating, ventilation, and air-conditioning (HVAC) systems using physics-based models in Building Performance Simulation environments as Digital Twins. We present a comparative framework to contrasts the current practice of replicating HVAC controls in Digital Twins and novel approaches to use Digital Twins for the specification of HVAC controls. The application of this framework to air handling units in a case study building underlines, that the replication of either designed or implemented HVAC controls in Digital Twins is work-intensive, associate with significant uncertainties, and inevitably results in a performance gap. The control-related performance gap can be closed when HVAC controls are identically transferred from a Digital Twin Prototype on building controllers which is demonstrated using the BPS software IDA ICE and the IEC 61131-10 XML exchange format.

Digital solutions based on information and communication technologies (ICT) provide many opportunities in buildings to achieve resource and energy efficiency. In general, these solutions enable either monitoring or advanced control of buildings. The ICT solutions' overall impacts on the environment are often presumed positive without a holistic approach based on life cycle thinking. The research on energy and indoor monitoring systems usually focuses on system performance and potential benefits rather than the entire system and it thus misses the life cycle impacts of the system itself. To address this limitation, the aims of this study are to assess life cycle environmental and resource impacts of a building monitoring system (BMS) and to identify hotspots in this system. The case study of KTH Living Lab represents an extensive BMS. It was applied and assessed using Life Cycle Assessment (LCA) methodology. The results show that wires, sensors and data acquisition equipment constitute hotspots for all the environmental and resource impacts assessed in this study. Thus, the impacts of these devices are important to consider by, e.g, building managers.

This work proposes a robust data-driven tube-based zonotopic predictive control (TZPC) approach for discrete-time linear systems, designed to ensure stability and recursive feasibility in the presence of bounded noise. The proposed approach consists of two phases. In an initial learning phase, we provide an over-approximation of all models consistent with past input and noisy state data using zonotope properties. Subsequently, in a control phase, we formulate an optimization problem, which by integrating terminal ingredients is proven to be recursively feasible. Moreover, we prove that implementing this data-driven predictive control approach guarantees robust exponential stability of the closed-loop system. The effectiveness and competitive performance of the proposed control strategy, compared to recent data-driven predictive control methods, are illustrated through numerical simulations.

Digital twin technology is an emerging technology within the built environment. There are yet many unexplored opportunities to utilize digital twins for facilitating the transformation towards a climate neutral building stock while also meeting the expectations from the building occupants. This article presents a case study of a digital twin, developed for an existing commercial building stock of campus areas in Sweden. The overarching purpose of the digital twin is to support both building occupants and building operators. This twofold human-centric approach represents a novel approach for building digital twins. The digital twin is based on 3D scanning and together with geospatial data, a real-like navigational indoor environment is created. Three innovative features are presented; the building analysis module, the digital twin mobile application and the building operations module. The results show that the digital twin improves the building occupant’s experience by supporting navigation and providing access to room booking system via this dedicated interface. Building management is also benefited by the digital twin through easier access to building data aggregated into one platform and a state-of-the-art analysis tool for optimizing the use of indoor space. The digital twin holds future potential to achieve operational excellence by incorporating feedback mechanisms and utilizing Artificial Intelligence to enable intelligent fault detection and prevention.

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In the light of global climate change and the current energy crisis, it is crucial to target sustainable energy use in all sectors. Buildings still remain one of the most energy-demanding sectors. Campus buildings and higher educational buildings are important to target due to their high and increasing energy demand. This building segment also represents a research gap, as mostly office or domestic buildings have been studied previously. In the quest for thermal comfort, a key stakeholder in building energy demand is the building occupant. It is therefore crucial to promote energy-aware behaviors. The building systems are another key factor to consider. As conventional building systems are replaced with smart building systems, the entire scenario is redrawn for how building occupants interact with the building and its systems. This study argues that behavior is evolving with the smartness of building systems. By means of a semi-systematic literature review, this study presents key findings from peer-reviewed research that deal with building occupant behavior, building systems and energy use in campus buildings. The literature review was an iterative process based on six predefined research questions. Two key results are presented: a graph of reported energy-saving potentials and a conceptual framework to evaluate building occupants impact on building energy use. Furthermore, based on the identified research gaps in the selected literature, areas for future research are proposed.

Energy-intense activities and the unpredictable and complex behavior of building occupants lead to an increase in building energy demand. It is, therefore, crucial to study underlying factors for building energy demand related to the users. Higher educational buildings are relevant to study for several reasons: they host the future workforce and citizens, they are predicted to increase in numbers, and they represent a building type less studied. Furthermore, green-rated buildings equipped with smart building systems also represent a research gap that is relevant to address since such a building design involves IoT-functionalities and digital features for the building occupants to interact with. There is also a conceivable risk that if the users know that the building is green-rated and technologically advanced, this may alter their perception of the building operation and thus their behavior. To study the relationship between building occupants and such green and smart educational structure, a survey was conducted in a Swedish higher educational building; as a result, 300 responses were collected and analyzed. The responses revealed that the building occupants act with energy awareness, and they are conscious about energy-saving behaviors. One building feature in particular was studied: the Digital Room Panels (DRPs). The DRP allows the building occupants to modify the indoor temperature and is, therefore, essential for thermal comfort. One key finding from the survey revealed that 70% of the building occupants did not know how the DRPs operate. This study argues that this result can be explained with a lack of communication and user friendliness. Inadequate interactions with building systems could also result in opportunities for energy saving might not be realized. The findings of this case study led to valuable recommendations and suggestions for future research endeavors.

This work proposes a robust data-driven predictive control approach for unknown nonlinear systems in the presence of bounded process and measurement noise. Data-driven reachable sets are employed for the controller design instead of using an explicit nonlinear system model. Although the process and measurement noise are bounded, the statistical properties of the noise are not required to be known. By using the past noisy input-output data in the learning phase, we propose a novel method to over-approximate reachable sets of an unknown nonlinear system. Then, we propose a data-driven predictive control approach to compute safe and robust control policies from noisy online data. The constraints are guaranteed in the control phase with robust safety margins through the effective use of the predicted output reachable set obtained in the learning phase. Finally, a numerical example validates the efficacy of the proposed approach and demonstrates comparable performance with a model-based predictive control approach.

This work proposes a robust data-driven predictive control approach for unknown nonlinear systems in the presence of bounded process and measurement noise. Data-driven reachable sets are employed for the controller design instead of using an explicit nonlinear system model. Although the process and measurement noise are bounded, the statistical properties of the noise are not required to be known. By using the past noisy input-output data in the learning phase, we propose a novel method to over-approximate exact reachable sets of an unknown nonlinear system. Then, we propose a data-driven predictive control approach to compute safe and robust control policies from noisy online data. The constraints are guaranteed in the control phase with robust safety margins by effectively using the predicted output reachable set obtained in the learning phase. Finally, a numerical example validates the efficacy of the proposed approach and demonstrates comparable performance with a model-based predictive control approach.