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[Display omitted] • Discusses the concept of building performance simulation versus new digital trends. • Reviews Artificial Intelligence, Machine Learning, Digital Twins, Cyber-Physical Systems, Internet of Things, Data Mining. • Explores common notions about differences, similarities, strengths and limitations of each concept. • Reviews emerging work on the interface between these concepts. • New concepts enjoy traction and energize the building performance research area. In an increasingly digital world, there are fast-paced developments in fields such as Artificial Intelligence, Machine Learning, Data Mining, Digital Twins, Cyber-Physical Systems and the Internet of Things. This paper reviews and discusses how these new emerging areas relate to the traditional domain of building performance simulation. It explores the boundaries between building simulation and these other fields in order to identify conceptual differences and similarities, strengths and limitations of each of these areas. The paper critiques common notions about these new domains and how they relate to building simulation, reviewing how the field of building performance may evolve and benefit from the new developments. [ABSTRACT FROM AUTHOR]

Digitalization of buildings and the use of IoT sensing and metering devices are steadily increasing, offering new opportunities for more autonomous, efficient, and flexible buildings. As part of this transformation and inspired by the added value demonstrated in other domains, the concept of a building digital twin that can monitor, simulate, manage, and optimize building operation has received increased interest. To aid such digital twin implementations, accurate and adaptable simulation models are required, which can effectively integrate and utilize the available data. However, traditional building modeling and digital practices, such as Building Information Modeling and white-box modeling tools, are not easily compatible with these requirements. This work presents an innovative and flexible energy modeling framework based on the SAREF ontology. With a basis in the SAREF4BLDG extension for buildings and the defined classes, different models are presented for a selection of typical systems and devices such as spaces, space heaters, dampers, coils, etc. Using the generic semantics and relations of the SAREF4SYST extension, a method for linking and simulating component models is then presented. A proof-of-concept of the modeling framework is provided, showing its application and feasibility to provide a dynamic simulation of the different systems and devices included in a demonstration case. Finally, a future line of work is identified considering the implementation of the modeling framework in an actual building case study, including integration with actual sensing equipment to demonstrate different digital twin services such as performance monitoring, strategy planning, and operational optimization. • An innovative energy modeling framework for building digital twins is presented. • The framework is built directly upon the SAREF ontology to improve interoperability. • Dynamic component models are identified for a selection SAREF4BLDG classes. • Algorithms are presented to link models and enable system simulation. • A proof-of-concept is provided through implementation on a demonstration case. [ABSTRACT FROM AUTHOR]

The classification of the energy efficiency of buildings with the use of the energy performance certificate, constitutes a common practice for the building energy assessment performance community. The main stream in the field is the so-called asset rating, that is the calculation of the energy performance of buildings, whereas another option for the definition of the energy class of a building relies on its measured performance, named operational rating. Despite the fact that significant progress has been achieved in the field of smart sensors and smart buildings, effective solutions related to the employment of Industry 4.0 practices, such as Building Information Modelling (BIM) or digital twins are not employed for the operational rating of buildings energy performance. This study aspires to introduce a comprehensive framework for the operational assessment of buildings energy performance, with the use of advanced tools and practices for building digitization. For the purpose of this study, a relevant set of operational indicators was employed, with the purpose to provide information, both to the landlords and the tenants, related to the actual energy performance of the building. The novelty of this study relies on the fact that for the extraction of the operational rating, a digital twin environment was used, employing smart sensors, real time measurements and a BIM environment. Within this study, the proposed concepts are demonstrated in a case study building of mixed used. This study aims to shed light to the development of practices for the remote and smart real time energy assessment of building units. [ABSTRACT FROM AUTHOR]

• A novel Digital Twin framework is presented to detect occupant discomfort. • The Digital Twin approach integrates BIM with sensor data & occupant feedback. • Bayesian network used to evaluate occupant comfort in non-residential buildings. • Nine ML algorithms are used for predictive maintenance. • XGB is up to 5% more accurate than other algorithms while RF is 96% faster than XGB. This study introduces a Bayesian network model to evaluate the comfort levels of occupants of two non-residential Norwegian buildings based on data collected from satisfaction surveys and building performance parameters. A Digital Twin approach is proposed to integrate building information modeling (BIM) with real-time sensor data, occupant feedback, and a probabilistic model of occupant comfort to detect and predict HVAC issues that may impact comfort. The study also uses 200000 points as historical data of various sensors to understand the previous building systems' behavior. The study also presents new methods for using BIM as a visualization platform and for predictive maintenance to identify and address problems in the HVAC system. For predictive maintenance, nine machine learning algorithms were evaluated using metrics such as ROC, accuracy, F1-score, precision, and recall, where Extreme Gradient Boosting (XGB) was the best algorithm for prediction. XGB is on average 2.5% more accurate than Multi-Layer Perceptron (MLP), and up to 5% more accurate than the other models. Random Forest is around 96% faster than XGBoost while being relatively easier to implement. The paper introduces a novel method that utilizes several standards to determine the remaining useful life of HVAC, leading to a potential increase in its lifetime by at least 10% and resulting in significant cost savings. The result shows that the most important factors that affect occupant comfort are poor air quality, lack of natural light, and uncomfortable temperature. To address the challenge of applying these methods to a wide range of buildings, the study proposes a framework using ontology graphs to integrate data from different systems, including FM, CMMS, BMS, and BIM. This study's results provide insight into the factors that influence occupant comfort, help to expedite identifying equipment malfunctions and point towards potential solutions, leading to more sustainable and energy-efficient buildings. [ABSTRACT FROM AUTHOR]

• Digital twin with AI to develop a predictive monitoring system that estimates CO2-eq and TVOC emissions from existing buildings. • The predictive monitoring system of CO2-eq emissions was developed using the design science research paradigm. • Real-life case study was used to validate the created solution. • This solution introduces interactive dashboard to predict Co2-eq based on varying humidity percentages and temperature degrees. The revolution of the industry 4.0 presents a new era of digital transformation for the construction industry, advancing towards the concept of digital twins, while on the other hand it faces the global challenge of reducing carbon emissions from operational assets. The current research gap in the application of digital twins for achieving net zero were reviewed in this study, highlighting its potentials for enhancement in the built environment, and emphasizing the need for demonstration of a use-case analysis for its adoption by the industry. This research presents a digital twin solution to automate the monitoring and controlling of equivalent carbon dioxide (eCO 2) emissions from existing assets through the integration of IoT, BIM, and artificial intelligence across a comprehensive solution, further validating its workability through a real-life use case analysis. The study revealed the significance of BIM and IoT, as essential components of a digital twin to visualise critical spatial information for enhanced facility management specifically for monitoring of indoor air quality of spaces, while also coalescing an AI-supported system to predict carbon emissions from the collected data through integration of machine learning features across the digital twin. The output of the entire solution is displayed as an interactive dashboard for observing trends and patterns, enabling stakeholders to implement effective data-driven retrofitting strategies. This research is a fundamental initiation for implementing digital twins to monitor emissions from existing assets, a step towards achieving the net zero targets. [ABSTRACT FROM AUTHOR]

Numerous buildings fall short of expectations regarding occupant satisfaction, sustainability, or energy efficiency. In this paper, the performance of buildings in terms of occupant comfort is evaluated using a probabilistic model based on Bayesian networks (BNs). The BN model is founded on an in-depth analysis of satisfaction survey responses and a thorough study of building performance parameters. This study also presents a user-friendly visualization compatible with BIM to simplify data collecting in two case studies from Norway with data from 2019 to 2022. This paper proposes a novel Digital Twin approach for incorporating building information modeling (BIM) with real-time sensor data, occupants' feedback, a probabilistic model of occupants' comfort, and HVAC faults detection and prediction that may affect occupants' comfort. New methods for using BIM as a visualization platform, as well as a predictive maintenance method to detect and anticipate problems in the HVAC system, are also presented. These methods will help decision-makers improve the occupants' comfort conditions in buildings. However, due to the intricate interaction between numerous equipment and the absence of data integration among FM systems, CMMS, BMS, and BIM data are integrated in this paper into a framework utilizing ontology graphs to generalize the Digital Twin framework so it can be applied to many buildings. The results of this study can aid decision-makers in the facility management sector by offering insight into the aspects that influence occupant comfort, speeding up the process of identifying equipment malfunctions, and pointing toward possible solutions. [ABSTRACT FROM AUTHOR]

Intelligent lighting systems and surveillance systems have become an important part of intelligent buildings. However, the current intelligent lighting system generally adopts independent sensor control and does not perform multi-source heterogeneous data fusion with other digital systems. This paper fully considers the linkage between the lighting system and the surveillance system and proposes a digital twin lighting (DTL) system that mainly consists of three parts. Firstly, a visualized operation and maintenance (VO&M) platform for a DTL system was established based on dynamic BIM. Secondly, the environment perception, key-frame similarity judgment, and multi-channel key-frame cut and merge mechanism were utilized to preprocess the video stream of the surveillance system in real-time. Lastly, pedestrians detected using YOLOv4 and the ambient brightness perceived by the environment perception mechanism were transmitted to the cloud database and were continuously read by the VO&M platform. The intent here was to aid timely adaptive adjustment of the digital twin and realistic lighting through the internet. The effectiveness of the proposed method was verified by experimenting with a surveillance video stream for 14 days. The key results of the experiments are as follows: (1) the accuracy rate of intelligent decision control reached 95.15%; (2) energy consumption and electricity costs were reduced by approximately 79%; and (3) the hardware cost and energy consumption of detection equipment and the time and cost of operation and maintenance (O&M) were greatly reduced. [ABSTRACT FROM AUTHOR]