The low accessibility to the information regarding buildings current performances causes deep difficulties in planning appropriate interventions. Internet of Things (IoT) sensors make available a high quantity of data on energy consumptions and indoor conditions of an existing building that can drive the choice of energy retrofit interventions. Moreover, the current developments in the topic of the digital twin are leading the diffusion of Building Information Modeling (BIM) methods and tools that can provide valid support to manage all data and information for the retrofit process. This paper shows the aim and the findings of research focused on testing the integrated use of BIM methodology and IoT systems. A common data platform for the visualization of building indoor conditions (e.g., temperature, luminance etc.) and of energy consumption parameters was carried out. This platform, tested on a case study located in Italy, is developed with the integration of low-cost IoT sensors and the Revit model. To obtain a dynamic and automated exchange of data between the sensors and the BIM model, the Revit software was integrated with the Dynamo visual programming platform and with a specific Application Programming Interface (API). It is an easy and straightforward tool that can provide building managers with real-time data and information about the energy consumption and the indoor conditions of buildings, but also allows for viewing of the historical sensor data table and creating graphical historical sensor data. Furthermore, the BIM model allows the management of other useful information about the building, such as dimensional data, functions, characteristics of the components of the building, maintenance status etc., which are essential for a much more conscious, effective and accurate management of the building and for defining the most suitable retrofit scenarios.

The existing building stock is globally responsible for 17.5% of greenhouse gas emissions due to their operation to achieve occupant satisfaction, thus requiring a vast intervention. However, reducing building stock emissions and optimizing building energy performance cannot be considered independently by the users’ well-being. The thermal comfort conditions and their monitoring represent a central issue that could optimize building energy usage while achieving good indoor environmental conditions. This document describes the first findings of ongoing research focused on the development of a building monitoring system, based on the integration of Building Information Modeling tools and sensor technology through Dynamo Visual Programming. Starting from the development of an Asset Information Model, which represents the virtual replica of a building that currently hosts the administrative offices of the municipality of Cagliari, the first step presented in this contribution shows a thermal comfort monitoring system, scalable and modular, that allows effective gathering and elaboration of data about comfort levels in each of the building’s rooms. The system proves to be a helpful support for facility managers to control building thermal comfort conditions and HVAC systems to assure their best operative status or plan suitable interventions to achieve it.

Currently, the management of public real estate stocks is one of the most challenging activities. In this case, facility management emerges as a strategic but complex discipline that requires a great amount of integrated and structured data and information of heterogeneous origin and nature. Starting from this premise, this paper focuses on how FM discipline can be supported by Building Information Modeling (BIM) and Geographic Information System (GIS) integrated methodologies and tools achieving high levels of effectiveness and efficiency in public building stock management. This led to the definition and development of a framework that mainly takes advantage of open-source tools (e.g., Quantum Geographic Information System—QGIS, and Dynamo) and non-proprietary data exchange formats like the COBie (Construction Operations Building Information Exchange) international standard. This methodological framework is capable of integrating different but strongly complementary tools, data, information, and expert knowledge for a multiscale approach. The methodology presented in this paper does not provide for an integral transfer of BIM data to the GIS platform but leads instead to an effective bidirectional integration between the two informative systems, by properly relating them and allowing for an easy switch from one system database to the other. The findings of experimental applications of the methodological approach to the former Macciotta pediatric hospital, a healthcare building owned by the University of Cagliari, are presented and discussed. Finally, the analysis about potentials and criticalities of the developed methodological framework aims to trigger the widest future development and dissemination of this research.

Several studies have proven the impact of the quality of indoor environmental conditions on human professional and cognitive performances. Additionally, building energy efficiency and indoor comfort have attracted increasing interest, encouraging the implementation of advanced digital technologies and platforms for a more efficient management of buildings. In this context, this study proposes a new framework for an effective BIM-IoT integration leading to a nearly Digital Twin (DT) relying on a BIM model equipped with regularly-generated IEQ reports summarizing statistics from real-time collected data to support facility managers’ decision-making. Despite the relevant literature on the subject, the proposed methodology introduces some novelties, as monthly results of Indoor Air Quality (IAQ) and thermal comfort evaluation are provided by open HTML reports automatically generated through a Python 3.10 code from sensor data. These reports are easily readable without needing any external platform to be visualized and are directly accessible through BIM models. The proposed methodology has been validated on a pilot case study, thus proving its efficiency, effectiveness, and robustness in terms of automation level, interoperability, adaptability, reliability, accuracy in data visualization, and management. The study shows promising results but also some issues that could be addressed through further development of the research.