Digital transformation offers significant potential to enhance the energy efficiency of public buildings through intelligent control systems. By creating comprehensive digital representations as digital twins of schools, offices, and other public facilities, we can leverage data to manage heating, ventilation, and air conditioning systems with unprecedented precision while maintaining optimal indoor air quality. The recent surge in energy prices has heightened the urgency and relevance of implementing these smart solutions across the public sector.

In an era where digital transformation is reshaping industries, the integration of digital twins in public property management stands out as a groundbreaking development.  The Data utilization for the development of energy efficiency and low carbon of the real estate – DaKiVE project aims to create a digital twin for refining data, enabling the collection, analysis, visualization, and simulation of smart solutions using data from selected buildings.  

The sites selected for this initiative include properties from different municipalities and Häme University of Applied Sciences (HAMK) own facilities. By leveraging these digital twins, we aim to support the digitalization of Small and Medium Enterprises (SME) in the sector. The primary goal is to enhance existing smart controls to better support public property maintenance organizations, thereby facilitating the extensive use of intelligent controls.

Core Components and Functionality

Digital Twins are digital representations of physical entities; they simulate the attributes and behaviors of a physical entity. They are most used for simulation, testing, maintenance and monitoring. Digital twins are composed of four main components: Sensors, models, IoT devices and data analytics tools. 

Sensors are the foundation of a digital twin as they gather data from the physical entity, which can be used to digitize the various behaviors of the physical entity. They can also be used to provide live data on the status of the entity. They are usually IoT-enabled to allow for connectivity and to allow data exchange with the model. Simulation models are the essence of digital twins. They process either sensor data or simulated inputs to determine how parameter changes affect outcomes. IoT devices facilitate communication and data transfer, while data analytics tools help process and analyze the collected information.

Digital twins, as shown in figure 1, can be broadly categorized into three types: parts twins, product twins, and system and service twins. Parts twins represent individual physical entities, such as sensors or discrete components. Product twins are representations of complete assembled products or equipment units, integrating multiple part twins to create a comprehensive model of an entire product. System and service twins focus on interconnected structures, processes, and workflows, such as HVAC systems or environments where various parts and products work together. This most complex category is particularly relevant in sophisticated environments where multiple components interact as an integrated system.

A digital twin of a building falls under the system and service twin category, as it incorporates various subsystems and operational processes rather than just a single entity. These digital twins are used throughout the building’s lifecycle, from design to maintenance and performance optimization.

• Design Phase: Digital twins enhance visualization and simulation, allowing architects and engineers to assess feasibility before construction begins.
• Performance Optimization: They enable simulations of energy consumption, component durability, and emissions, aiding in sustainability and efficiency.
• Lifecycle Management: By continuously monitoring building systems, digital twins support predictive maintenance, reducing costs and extending asset longevity.

By integrating real-time data and simulations, digital twins play a crucial role in improving decision-making, enhancing sustainability, and optimizing the performance of buildings over time.

Another application of digital twins can be found in the operations of a building where, in tandem with building management systems (BMS), control and monitoring can be established over various building systems. Further, real-time data on environmental factors such as temperature and lighting can be obtained, which can then be used to adjust these parameters to maximize occupant comfort. Integration of digital twins with the building information modeling (BIM) systems is an application that can yield positive results and assist building managers in multiple spheres of building operation  

Building maintenance and upkeep can also be enhanced with the use of digital twins; digital twins can help in maintaining detailed records of building components, allowing for effective asset management. They can also aid in decision making as metrics, such as performance data and maintenance history, can be stored digitally. Further, building workers can access digital twins remotely, which allows for remote real-time diagnostics and troubleshooting. The condition of the building can also be monitored, allowing for optimal usage of building components whose replacement would earlier be based on fixed intervals, where, despite being in optimal condition, the components would have been changed.   

Project Development Stages

Creating a digital twin that can deliver all the intended benefits involves several steps. These steps have been followed during the implementation of the Data utilization for the development of energy efficiency and low carbon of the real estate – DaKiVE project:

• Mapping the Objects: Objects that shall be connected to the digital twin have to be identified and mapped. Such as properties of public organizations and pilot sites of the participating companies. 
• Connecting and Analyzing Data: Selected objects will be connected to the digital twin, enabling seamless data storage and analysis. HAMK will develop a data platform for this purpose to meet requirements in digital twin needs and the creation of different solutions.  
• Innovating Data-Based Solutions: We will brainstorm and pilot new data-driven solutions to support public organizations in managing their properties efficiently. This initiative will also assist companies in developing new services and innovations. The outcome will be a digital experimentation environment, engaging industry companies and fostering an innovation ecosystem that develops data-driven services.  

Project outcome and impact

The evolution of digital twins represents a transformative approach to managing built environments, particularly in the public sector, where energy efficiency and resource optimization are increasingly critical. As demonstrated throughout this article, digital twins offer comprehensive development stages for understanding, monitoring, and enhancing building performance across their entire lifecycle.

This project will culminate in the publication and distribution of resulting data-driven services, making them accessible to public organizations and companies. By ensuring the results are publicly available, we enable all organizations to benefit from or further develop these innovative services. This open approach maximizes the impact of our work and fosters continued innovation in the field.

By harnessing the power of digital twins, our work not only aims to revolutionize public property management but also supports the broader goal of digital transformation across sectors. Through strategic collaboration and continuous innovation, we are paving the way for smarter, more efficient property management solutions that address current challenges while preparing for future demands. The digital twin approach represents not just a technological advancement but a fundamental shift in how we conceptualize and manage our built environment for sustainability and efficiency.

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This text has been written as part of the “Data utilization for the development of energy efficiency and low carbon of the real estate – DaKiVE” project. The project is co-funded by the European Union and implemented in cooperation by Häme University of Applied Sciences and the Finnish Environmental Institute SYKLI.

You can read more about the project on its website.