The digitalization trend in buildings is accelerated with advancements in information and communication technologies (ICT). These technologies enable many opportunities in buildings to achieve resource and energy efficiency based on either monitoring or advanced control of buildings. The ICT solutions’ overall impacts on the environment are often presumed positive without an assessment based on life cycle thinking. The research on ICT solutions in buildings is mainly related to occupancy, energy, and indoor environmental monitoring with the focus on technical performance and potential benefits while the environmental impacts are often overlooked. This paper analyses two case study buildings in Sweden with building monitoring systems (BMS) based on life cycle assessment (LCA) methodology. The two cases were chosen to represent a range between simple and more sophisticated. The results show that sensors and wires contribute significantly to several impact categories and the impacts of electronics used for data acquisition are also important. The climate impact of the production phase is significantly higher than the use and waste management. Depending on the energy mix, the minimum required energy saving to offset the climate impacts of the BMSs are 5% (standard BMS) and 24% (extensive BMS). This is comparable to the energy savings that often are assumed when building monitoring systems are introduced. Thus, the direct impacts of the enabling technologies for digitalization might be significant and it is crucial to employ a life cycle perspective when assessing the environmental benefits of digital solutions in buildings.

Occupant-building interactions significantly influence indoor environmental quality and energy use, yet they are still often represented in building energy simulation with simplified or schedule-based assumptions. This study quantifies how occupant-specific (heterogeneous) window-opening and window-closing behavior affects space heating energy use in a Swedish residential building under comparable conditions. Using four winters of moni tored data from several single-occupant apartments, occupant-specific logistic regression models are developed for window opening and closing actions. It is shown that common drivers (e.g., indoor air quality and time of day) coexist with substantial inter-occupant differences. These models are then integrated into a closed-loop co-simulation (IDA ICE-MATLAB) with a calibrated building model to compare occupant profiles under identical boundary conditions and against a consistent closed-window reference case. Impacts are reported as a normal ized heating energy increase relative to the closed-window case, providing a direct comparison across occupant profiles and different scenarios. The results show that window operation can lead to large and highly variable heating losses, with some occupant profiles increasing space heating energy use by up to threefold relative to the closed-window baseline. These findings demonstrate that representing inter-occupant heterogeneity is essential for reliable energy performance assessment and occupant-centric building control design.

Digitalization of the building sector has become a critical topic of interest, prioritizing the decarbonization goals set for this decade. To achieve net zero emissions by 2050 according to the Paris Agreement, all new buildings and 20% of existing buildings are expected to be fully decarbonized by the end of 2030. The digital twin is an emerging concept where a physical asset and its digital copy interact in real-time, enabling various benefits such as the possibility for monitoring and analysis. Various studies have been conducted to understand how digital twins can bring value in decarbonizing buildings. In the context of evolving electricity grids, there is an evident gap in research on digital twins for grid-interactive buildings. These buildings can deliver power back into the grid and participate in the ancillary market, thus helping to avoid demand peaks, also providing flexibility. This paper conducts a systematic literature review using PRISMA guidelines on digital twins for smart grid interactive buildings, which the existing literature lacks. The study then develops a classification framework to understand the difference between a digital twin and other digital representations in the context of grid-interactive buildings based on the existing literature. It identifies and critically compares the best software tools and artificial intelligence techniques that can be used for creating a digital twin for a grid interactive building with optimal precision and accuracy in real-time. Finally, the review results are analyzed and used to develop a recommendation roadmap for developing a digital twin for a grid-interactive building.

This paper investigates non-intrusive occupancy detection methods for residential buildings using environmental sensor data from the KTH Live-In Lab in Stockholm, Sweden. Three machine learning approaches, namely, logistic regression (LR), support vector machines (SVM), and long short-term memory (LSTM) network enhanced with an attention mechanism, are evaluated in terms of predictive performance and computational complexity. The analysis considers the trade-off between sensor availability (investment cost) and prediction accuracy in real applications, as well as the models' cross-apartment generalizability. Hyperparameters for both the SVM and LSTM models are optimized using Bayesian optimization. All three models are evaluated on data collected from apartments not used during training, and on data generated from a calibrated digital model of the testbed. Results show that all models achieve comparable performance on the same-apartment test data (accuracy of approximately 0.83, F1 score of approximately 0.86). When assessed on cross-apartment data, the LSTM model demonstrates the strongest generalization capability (accuracy of 0.84, F1 score of 0.85), while LR provides a competitive, low-complexity alternative for applications that do not require cross-apartment generalization.

Space heating in buildings accounts for 10% of the global CO2 footprint. The widespread adoption of energy-efficient heating technology, e.g., heat pumps, could help reduce this figure, but technology alone may not suffice to reach carbon neutrality. Additionally, human occupants have an important role to play by adopting sustainable heating behaviors, e.g., avoid excessive window opening in the winter or (pre-)heat their units while clean energy is abundant. Thus far demand response policies aimed at promoting these behaviors have been monetary, which discriminates against low-income households and exposes human occupants who do not actively engage with real-time control signals to financial risks. This paper instead investigates the suitability of a non-monetary karma economy for promoting sustainable heating behaviors. Karma leverages the repeated and dynamic nature of heating energy allocations to attain climate targets both fairly and efficiently over time without resorting to financial means. As a first step towards experimentally validating the karma concept with real human occupants in the KTH Live-In Lab, we perform a simulation study on a digital model of the Live-In Lab. The study provides initial estimates of expected effects to guide the design of human-in-the-loop experiments, as well as assists with designing and tuning the karma economy in this context. As a specific example, we investigate how incorporating consumption memory in the form of karma affects window opening behaviors in comparison to conventional memory-less heating operation.

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.

Building and construction sector is responsible for 40% of the total energy consumption and 36% of the total greenhouse gas emissions in the European Union. Digital twin is an emerging digital tool that facilitates building management through data interactions using sensor readings between a physical building and its digital model and improves operation and enhances transparency. However, since the digital twin technologies are not mature and has several challenges associated with it, such as need for extensive data, it is necessary to conduct a systematic literature review on its application to buildings and smart grids. The majority of the current studies look into how digital twins can be used for the management of normal residential or commercial buildings that are connected to conventional electricity grids with little scope for bidirectional power flow. This study conducts a systematic literature review to map the current landscape of research on digital twins in grid-interactive buildings, with a focus on identifying the software tools used in the creation of digital twins for improving energy efficiency. The study uses scientific databases like Scopus and Web of Sciences and has been carried out in accordance with PRISMA guidelines that specify the different steps involved in the methodology to conduct systematic reviews. Autodesk Revit and Artificial Neural Networks emerged as the most common software and technique, based on previous works.

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 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.

Digital twin technology is an emerging technology within the built environment. Yet, there are many unexplored opportunities to utilize digital twins for facilitating the transformation toward 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 two-fold 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 the 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.

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.

Window-opening and window-closing behaviors play an important role in indoor environmental conditions and therefore have an impact on building energy efficiency. On the other hand, the same environmental conditions drive occupants to interact with windows. Understanding this mutual relationship of interaction between occupants and the residential building is thus crucial to improve energy efficiency without disregarding occupants' comfort. This paper investigates the influence of physical environmental variables (i.e., indoor and outside climate parameters) and categorical variables (i.e., time of the day) on occupants' behavior patterns related to window operation, utilizing a multivariate logistic regression analysis. The data considered in this study are collected during winter months, when the effect on the energy consumption of the window operation is the highest, at a Swedish residential building, the KTH Live-In Lab, accommodating four occupants in separate studio apartments. Although all the occupants seem to share a sensitivity to some common factors, such as air quality and time of the day, we can also observe individual variability with respect to the most significant drivers influencing window operation behaviors.

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.

The adoption of innovation in the building sector is currently too slow for the ambitious sustainability goals that our societies have agreed upon. Living labs are open innovation ecosystems in real-life environments using iterative feedback processes throughout a lifecycle approach of an innovation to create sustainable impact. In the context of the built environment, such co-creative innovation and demonstration platforms are needed to facilitate the adoption of innovative technologies and concepts for more energy-efficient and sustainable buildings. However, their feasibility is not extensively proven. This paper illustrates the implementation and demonstrates the feasibility of the Living Labs Triangle Framework for buildings living labs. This conceptual framework has been used to conceive the KTH Live-In Lab, a living lab for buildings. The goal of the Live-In Lab was to create a co-creative open platform for research and education bridging the gap between industry and academia, featuring smart building demonstrators. The Living Lab Triangle Framework has been deployed to meet the goals of the Live-in Lab, and the resulting concept is described. This paper then analyses the methodological and operational results introducing performance metrics to measure the economic sustainability, the promotion of multidisciplinary research and development projects, dissemination and impact. The results are completed with a SWOT analysis identifying its current strengths and weaknesses. The results collected in this work fill a missing gap in the scientific literature on the performance of living labs and provide empirical evidence on the sustainability and impact of living labs.

This study investigates how information about the controls of heating, ventilation and airconditioning (HVAC) systems can be gathered from building automation system (BAS) for the creation of digital twins in Building Performance Simulation (BPS). The concept of a digital twin in BPS is commonly used for monitoring applications such as fault detection or performance gap analysis. Often emphasis is put on physical properties of the building envelope etc. or user behavior. In modern buildings, automation systems play an important role to guarantee user comfort requirements as well as an energy efficient operation. To replicate real HVAC controls in BPS, the underlying control logic has to be known. To gather this information in operation we have investigated three sources: firstly, documentation from the design phase including already existing simulation models, secondly, the control code on automation systems and thirdly, reverse engineering from measured data. The study focuses on air handling units (AHUs) and is based on experiences in state-of-the-art building demonstrators from research projects in Sweden and Germany.

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 paper presents a path towards the implementation of a Digital Twin for campus environments. The main purpose of the Digital Twin is to accomplish an advanced analytical tool, which supports building owners, building operators and building users to reach an improved performance of the building. Digital Twins is new to the building and the real estate industry, hence research within this field is scarce. This paper contributes to the research by providing a methodology to implement a Digital Twin of an existing building stock of campus areas in Sweden. The main results obtained so far are presented. They indicate that the potential of a Digital Twin expands beyond the aspects of a navigational digital 3D model, including a state-of-the-art app that is developed from the Digital Twin platform.

Digitalization offers new, unprecedented possibilities to increase the energy efficiency and improve the indoor conditions in buildings in a cost-efficient way. Smart buildings are seen by many stakeholders as the way forward. Smart buildings feature advanced monitoring and control systems that allow a better control of the buildings’ indoor spaces, but it is becoming evident that the massive amount of data produced in smart buildings is rarely used. This work presents a long-term evaluation of a smart building testbed for one year; the building features state-of-the-art monitoring capability and local energy generation (PV). The analysis shows room for improving energy efficiency and indoor comfort due to non-optimal control settings; for instance, average indoor temperatures in all winter months were above 24 °C. The analysis of electricity and domestic hot water use has shown a relevant spread in average use, with single users consuming approximately four times more than the average users. The combination of CO2 and temperature sensor was sufficient to pinpoint the anomalous operation of windows in wintertime, which has an impact on energy use for space heating. Although the quantification of the impact of users on the overall energy performance of the building was beyond the scope of this paper, this study showcases that modern commercial monitoring systems for buildings have the potential to identify anomalies. The evidence collected in the paper suggests that this data could be used to promote energy-efficient behaviors among building occupants and shows that cost-effective actions could be carried out if data generated by the monitoring and control systems were used more extensively.

This work investigates the feasibility of using input-output data-driven control techniques for building control and their susceptibility to data-poisoning techniques. The analysis is performed on a digital replica of the KTH Livein Lab, a non-linear validated model representing one of the KTH Live-in Lab building testbeds. This work is motivated by recent trends showing a surge of interest in using data-based techniques to control cyber-physical systems. We also analyze the susceptibility of these controllers to data-poisoning methods, a particular type of machine learning threat geared towards finding imperceptible attacks that can undermine the performance of the system under consideration. We consider the Virtual Reference Feedback Tuning (VRFT), a popular data-driven control technique, and show its performance on the KTH Live-In Lab digital replica. We then demonstrate how poisoning attacks can be crafted and illustrate the impact of such attacks. Numerical experiments reveal the feasibility of using data-driven control methods for finding efficient control laws. However, a subtle change in the datasets can significantly deteriorate the performance of VRFT.

Digitalisation is an increasingly important driver of urban development. The ‘New Urban Science’ is one particular approach to urban digitalisation that promises new ways of knowing and managing cities more effectively. Proponents of the New Urban Science emphasise urban data analytics and modelling as a means to develop novel insights on how cities function. However, there are multiple opportunities to broaden and deepen these practices through collaborations between the natural and social sciences as well as with public authorities, private companies, and civil society. In this article, we summarise the history and critiques of urban science and then call for a New Urban Science that embraces interdisciplinary and transdisciplinary approaches to scientific knowledge production and application. We argue that such an expanded version of the New Urban Science can be used to develop urban transformative capacity and achieve ecologically resilient, economically prosperous, and socially robust cities of the twenty-first century.