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 gridinteractive 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.

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 monitored 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 normalized 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.

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.

Global floor area is increasing every year which is subsequently leading to an increase in electricity and heating demand in buildings. Residential buildings have huge potential for energy savings and there is an immediate need to decarbonize them by the end of 2050. Machine learning is finding application across all fields and will thus have an important role to play in the building sector also. One of the important challenges that building owners need to tackle is occupancy detection in residential apartments which can help save considerable amounts of energy and costs. However, occupancy is highly variable, and it is difficult to quantify and predict occupancy because of the random and individualistic nature of humans. In addition, scalable approaches for occupancy detection should prioritize data from common and cost-effective sensors like temperature sensors. In contrast to existing literature which has stated that occupancy detection based on the data from a single environmental sensor is not appropriate for obtaining good results, this paper aims to detect occupancy in a real residential building using only indoor temperature as the feature to train the model. Different machine learning models and techniques are studied and tested to understand how the accuracy of occupancy detection can be increased. With the right techniques, it has been possible to obtain promising results in the form of an accuracy of 95% using machine learning models and only indoor temperature to train it.

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.

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.

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.

Air-handling units (AHUs) have become indispensable parts of heating, ventilation, and air conditioning (HVAC) systems. AHUs are also significant energy consumers due to the function of their several actuators. Many recent works focus on improving the control techniques of AHUs to provide better indoor comfort with lower energy consumption. However, due to its inherent structure, it is complex to design an optimal and adaptive control for AHU that fulfills this mission in all operation conditions. Model predictive control (MPC), in this context, has been in focus in many contributions recently. However, designing a multi-input multi-output (MIMO) MPC for AHU optimal control is not a trivial task due to the difficulty of having a high-fidelity mathematical model. This study proposes and validates a data-driven nonlinear MPC with MIMO architecture. The proposed MPC is based on the sparse nonlinear dynamic of AHU built upon operation data of a real AHU installed in the KTH live-in lab. In contrast to the classical approaches, the proposed MPC adjusts simultaneously five different actuators to control the supply temperature. This article presents a simulation study for the performance of the proposed MPC framework under different control configurations.

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.