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Digital twins are virtual representations of an object, process or system that can be used to run simulations to optimise efficiency. Cities can use them to plan transportation systems, prepare for natural disasters, and identify optimal locations to install solar panels.
Industry, Innovation And Infrastructure
Sustainable Cities And Communities
Digital Twins are used increasingly to support urban planning processes – by visualizing urban data, show-casing future scenarios and many other use cases. In general Digital twins are virtual representations of an object, process or system that can be used to run simulations to optimise efficiency and examine what-if scenarios. The technology has been primarily used for manufacturing to test products (e.g. as of 2018, GE had 1.2 million digital twins for 300,000 types of assets) but is quickly expanding to buildings, supply chains and entire cities as digital planning technology advances (Castro, 2019). Integrating data from the Internet of Things (IoT) with the advanced modelling capabilities of technologies such as geospatial information systems (GIS), virtual and augmented reality (VR/AR) and building information modelling (BIM) allows governments and industry to create predictions of how systems will react and respond to real-world data. Creating a feedback loop between the virtual and real worlds results in substantial improvements of processes and impacts, with time-saving and financial benefits.
The concept of digital twins is not new; for example, NASA has been running simulations of spacecraft for decades, but the rapid growth of connected sensors and endpoints with the rise of the IoT and advancements in artificial intelligence has opened up a myriad of possibilities for the planning and analysis tool. Potential uses for digital twins are still being imagined. Uses for cities currently include using digital twins to plan transportation systems, prepare for natural disasters and identify optimal locations to install solar panels. Future uses could include predicting how a disease will spread and informing optimal lockdowns and hospital reservations or using the tool to facilitate collaborations with other cities that have shared problems and mutual goals.
Problems to be solved:
Ease complex decision-making
Enable more efficient urban planning
Enable more efficient project implementation and monitoring
Create better preparedness for emergency situations
Make urban data easier to understand
The main goal of Digital Twins is to ease urban planning processes, use data and increase transparency. Besides that, the solution achieves the benefits listed below. Whereas some benefits are likely to be fulfiled with a basic implementation of the solution, the fulfilment of the potential benefits depends on the functions implemented in a specific project.
Enhanced data analysis
Facilitating citizen engagement
Improving personnel efficiency
Improving life quality
Enhanced data collection
Increased data transparency
Enhanced data collection
Efficient integration of renewables
Functions help you to understand what the products can do for you and which ones will help you achieve your goals.
Each solution has at least one mandatory function, which is needed to achieve the basic purpose of the solution, and several additional functions, which are features that can be added to provide additional benefits.
Connect with data about the city
accessing data (e.g. from sensors) about the different components of a city
use real time data to create simulations that can predict how a product or process will perform
Provide information to optimise processes
provide decision makers with information on how to optimise simulated products or processes
Prepare for emergency situations
Simulate disaster response to best prepare
Communicate to stakeholders
features enabling citizen participation and stakeholder collaboration
Products offering these functions
Digital Twin for PEDS
This virtual twin focuses on all energy demands (electricity, district heating) and on-site electricity production from PV.
The simplest form of digital twin, this variant displays the live state of a physical asset or process to human observers. The value provided is the ability for a city to have people act on the information provided. For example, a city could have a digital twin that tracks the condition of streets and roads, letting observers know when the conditions require maintenance to be done.
Interactive digital twins take control of at least one aspect of the physical asset or process to achieve better performance, either from internal monitoring or more complex analysis. For example, thermostats in public housing units report when the temperature has gone above or below a certain threshold, triggering automatic responses to adjust the amount of heat being produced by the heating system.
This is the most complex type of digital twin. Predictive digital twins monitor state information over time to provide augmented information such as recommendations and/or warnings to human observers and/or other digital systems. For example, to expand the street monitoring system example from the Supervisory Variant, the system could predict when streets will require maintenance based on the amount of traffic the streets encounter. The difference here is that in the Supervisory example, the city needs to rely on their own interpretation of the street conditions whereas in the Predictive case, the city is directly informed when the conditions will require maintenance based on information that was not available in the Supervisory case. This reduces the amount of human judgment required.
Digital twins are supported by cities with:
IoT sensors embedded in the city’s core services
High levels of connectivity and useable data
GIS system in place
Digital Twins of smart cities is a new application of several technologies that need to be interoperable and high-performance. A capable and innovative team, with in-depth knowledge of how these technologies and their data function, will support a city in getting the most out of their digital twin. A brief overview of some of these technologies that are integrated into digital twins follows:
Geospatial Information Systems: The core spatial modelling technology of a city’s digital twin, GIS connects different kinds of geospatial data to create a single view and provide advanced analytics of the system.
Building Information Modelling: When integrated with GIS, BIM provides a rich dataset for the built environment to create more accurate models. Real-time data requires interoperability with the Internet of Things.
Augmented or Virtual Reality: Enhances real-life perception of digital twins. This can be particularly useful in collaborative and participatory processes in urban planning.
Stakeholder Map of an Urban Digital Twin (BABLE, 2021)
Across all industries, the global digital twin market size was valued at USD 3.1 billion in 2020 with an expected CAGR of 58% until 2026, when it is projected to reach USD 48.2 billion. The response to the COVID-19 pandemic is expected to be a major driver of the digital twin market growth, in particular in the healthcare and pharmaceutical industries, as well as for cities to better monitor outbreaks and respond to the changes in daily life brought by the pandemic (Markets&Markets, 2020).
Digital Twins on the city level are still a nascent technology and the cost structures will vary widely based on how advanced the software and hardware the city decides to invest in. One example, Virtual Singapore, had a budget of $73 million for developing their digital twin platform and for researching the tools and technologies required (NRF, 2020).
The creation of this solution has been supported by EU funding
Photorealistic Digital Twin to visualise urban development
The aim was to visualise a future school extension in the most immersive and photorealistic way to share it with the local community.
In the coming years, the city of Essen will rely on artificial intelligence (AI) for the damage assessment of roads. Named TWIN4ROAD, the Office for Geoinformation, Surveying and Cadastre together with Point Cloud Technology, HPI Potsdam and Straßen.NRW launched a three-year research project.
Increasing Energy Efficiency in Buildings Using AI
TPC identified areas of improvement for the management of the HVAC systems of two buildings. We performed a building energy performance analysis using artificial intelligence techniques to make an accurate prediction of the energy needs of these buildings to reduce their energy inefficiencies.
BIMROCKET is an open source platform for managing Building Information Modeling (BIM) projects, a collaborative working methodology for the construction industry. It allows viewing and editing building models and storing BIM projects in an OrientDB database.
Communicate Urban Real Estate Development Projects With the Help of Digital Twins
Fällanden did a municipality development project and wanted to offer their population a realistic, interactive visualisation with which the context of the newly planned buildings could be clearly shown. With the resulting clarity, a more participatory and faster decision-making process was achieved.
An intelligent and connected public space collects data in public areas and displays or reacts on the data. The data can be securely transferred via Wi-Fi or other similar technologies to be, i.e. combined with a central system.
The Internet of Things (IoT) is a constantly and rapidly evolving technological advancement that aims to increase the connectivity of our daily activities. IoT enables more effective and informed decision-making through improved data analysis and increased interconnectedness.
Broadband Wireless Mesh Networks enable seamless IoT applications by carrying fibre-like speed wirelessly. By hosting next generation of smart devices and by building private network for civil services, cities are building real-time digital services and improving safety and service delivery.