Digital Twins for Smart Buildings and Industrial IoT

Building development inevitably brings up the discussion around Smart Buildings, and with that, IoT and Digital Twins, are not far away.

This article aims to clarify the concept of a Digital Twin and distinguish between exaggerated claims and genuine applications. It concludes by outlining some of the key benefits. The primary takeaway from this article is to develop an understanding and appreciation of the value that a Digital Twin brings to Smart Building or Industrial IoT projects.

The Digital Twin for Smart Buildings integrates BIM, traditional building technologies (power, light, HVAC), IoT sensors (for air quality, occupancy, etc.), and other operational systems. It enables data analytics and control capabilities as well as the gathering of data for ESG purposes. It is a necessity for any modern buildings and advanced industrial plants.

In addition to capturing data from various systems for analytics and reporting, the Digital Twin allows control over these systems and facilitates simulations for "what if" analysis. This technology contributes to reducing operational and maintenance costs and minimizes the need for human intervention and labor. These functionalities are supported by AI, which operates the systems and enables interaction through natural language communication.

The concept of Smart Buildings has evolved significantly in the past decade. Previously, Smart Buildings primarily focused on energy efficiency. Today, the term encompasses a comprehensive approach that includes architectural design, sustainability, technology, user experience, comfort, and hybrid working. The goal is to integrate these elements to support human health, wellbeing, and productivity for a better world.

Smart Buildings need to broaden their approach on sustainability, not just energy efficiency and reducing CO2 emissions. This involves using advanced materials and construction techniques, circularity of materials, and alternative energy solutions like solar panels, power regeneration systems, aquifer thermal storage, and EV charging stations. Modern buildings should also have smart grid capabilities to optimize energy use based on cost and availability, with the potential for AI-driven energy purchasing.

Core building technologies like power distribution, heating, cooling, air-conditioning, lighting, and elevators are undergoing significant digital transformation (you simply can’t buy them without embedded digital capabilities and external connectivity). Manufacturers are now integrating sensors, communication, and computing capabilities into these systems by default, driven by lower costs, remote monitoring, predictive maintenance, and the increasing demand from operators, building owners, and tenants for system integration and data analytics to optimize building performance.

Smart Buildings have become essential for attracting talent, driven by larger corporate tenants and influenced by the 'war-on-talent', CSR initiatives, and COVID-19. The need for energy efficiency and reduced CO2 footprint, along with certifications like BREEAM, LEED, Well, and Wired Score, will continue to increase demand for smart technologies in new and redevelopment projects. Employees now expect safe, comfortable and flexible work environments with amenities like food services, dry cleaning, and parcel delivery, all integrated via web browsers or smartphone apps.

A Smart Building is an assembly of various systems that creates integration challenges for data gathering, control, and monitoring. System integration gathers data from siloed data stores and formats from individual systems and facilitates automation and control. Building operators, owners, and facility managers are using smart building platforms and integrated workplace management systems to manage these complex systems. The need for data, ESG and compliance reporting as well as exposing building operations to tenants, necessitates a data analytics and reporting platform.

The demand for Digital Twins in Smart Buildings is gaining attention due to seeds planted years ago. Multiple factors are driving their application, such as the rise of SCADA or BMS systems, and the proliferation of data-generating systems like building access, parking, and IoT sensors. Efficient and error-free commissioning of buildings with all of its technology is crucial.

Digital Twin technology is now being applied to Smart Buildings, following its success in cost savings and productivity in manufacturing and industrial sectors.

Digital Twin

A digital twin is a virtual model that mirrors the real-time status of a physical object or process. The concept, introduced by Michael Grieves in 2002, was practically defined by NASA in 2010 to enhance spacecraft simulations. Digital twins evolved from advancements in product design, moving from handmade drafts to CAD and model-based systems engineering.

According to Wikipedia, a Digital Twin is a real-time virtual representation that serves as the digital counterpart of a physical object or process. The key elements are its bidirectional nature—reflecting changes in both physical and digital realms—and its simulation capabilities. This involves not just knowing the attributes of the physical object but understanding their relationships and dependencies. Effective simulation requires data gathering and modeling the behavior and interactions of entities.

This definition distinguishes true Digital Twins from marketing claims. The real digital twin includes bidirectional communication for receiving and responding to data, along with simulation capabilities enabled by computing power and an ontology to understand relationships and dependencies. 3D models of a building or industrial process, regardless of their impressive designs and navigation capabilities, do not constitute a Digital Twin. They are merely a representation of it.

A genuine Digital Twin isn't just a static model displaying data in a fancy format. It requires computing power, storage, data ingestion capabilities, and an ontology to model the digital twin.

Digital Twin Ontologies

Digital Twin models buildings, spaces, processes, systems, and even people using an ontology. In computer science, ontologies define categories, properties, and relationships among concepts, data, and entities within a specific domain (e.g. smart buildings), illustrating how these elements relate.

Regarding Digital Twins and specifically Smart Buildings, these are typically based on either OWL or RealEstateCore frameworks. A comprehensive Digital Twin solution serves as a data integrator, amalgamating rich and complex data from various sources into a unified model that encompasses IoT data, structural information, geometry, behavior, temporal data, and business data. Digital Twins operates within a digital service facilitated by cloud computing platforms, such as Azure Digital Twins.

A Digital Twin uses the Digital Twin Description Language (DTDL), a JSON-based language, to create descriptive models. RealEstateCore, an open-source DTDL-based ontology for the real estate sector, was initially developed by Sweden's Jönköping University and is now maintained by the RealEstateCore consortium.

Benefits

Despite the buzz around digital twins, a digital twin is an enabler of value and not itself the desired outcome or a product. Digital Twins provide a means for business to leverage advances and solve real-world problems. Digital Twins with real-world data enable (new) digital business models.

Digital Twins streamline processes and data reliability through:

·       A digital thread that enables data continuity

·       Data provenance and continuity to track and explain data

·       A real-time continuum across otherwise disconnected CapEx and OpEx

Digital Twins enhance enterprise digital culture through:

·       Collaboration across many stakeholders from historical silos

·       A way to improve the workforce skills for digital and attract digital natives

A digital twin can boost efficiency and safety through:

·       Increased productivity

·       Improved product design and quality through simulation of what-if scenarios

·       Improved safety by anticipating and avoiding potential operator mistakes

·       Immersive user experience for service operators, thereby reducing downtimes.

Digital twins Improve customer experience through:

·       Streamlined product innovation

·       Remote troubleshooting with context

·       Digital business models (e.g., services)

Digital twins share Data Across Silos

One of the main benefits of using a digital twin is that you can bridge data across the different silos that exist in any Smart Building. Different systems have traditionally captured and stored data in their own silos. The Digital Twin can collect data from sensors and any other sources instead of each silo capturing and maintaining its own data. The Digital Twin model understands the relationship between all the siloed data sources.

Digital twins manage Operations in Real-Time

A Digital Twin creates situational awareness by building a holistic picture of the Smart Building it represents. It does more than storing and providing insights based on historical data. It enables real-time operations by providing real-time visibility, getting recommendations, and creating actions based on events in the data.

Digital twins support Simulations & Experiments

The Digital Twin model provides a mechanism to do simulations, where you can experiment on the Digital Twin rather than on the real entity. Simulations are particularly useful when applying physical and engineering models like finite element methods, where you want to bring in data from multiple sources. When you have real-time data, you can apply predictive analytics to your Digital Twin. Then you can determine when an asset is likely to fail or how to optimize your operations. You can also make changes to the physical entity based on the insight you gained from applying the analytics to the Digital Twin.

Digital Twins support Real-Time Analytics

As a Digital Twin ingests data in real-time, it can apply AI and machine learning to look for anomalous behavior, predict future states, and optimize production. This advanced real-time analytics is the first step to getting the most value out of your Digital Twin.

Digital twins support Decision Support

This additional layer of intelligence can be used to display predictions from your Digital Twin. It provides decision-making support for your engineers when they need to make real-time decisions. By providing details and predictions about metrics like remaining useful life or stock levels, you can empower your team to respond faster to critical business events.

Digital twins support prescriptive Analytics & Recommendations

The final way to leverage AI and machine learning in your Digital Twins is to use them for prescriptive analytics and to create recommendations on the best action to take next based on their predictions.

Digital twins Improve Trustworthiness

Digital Twins help improve the trustworthiness of your systems by giving you a global view of security, reliability, and availability. This improved visibility can help you identify any blind spots in your system.

Digital twins Enable Collaboration

Many organizations now require cross-functional teams. They expect traditional operational technology roles like engineering and maintenance, as well as IT, automation engineers, and data scientists, to work together to improve operations. Having a Digital Twin can enable collaboration across your teams by providing a shared understanding of what an entity looks like when it is represented in a Digital Twin model.

Want to know more, what does an ontology look like, the bridges digital twins builds between systems, how digital twins break down the data silo’s and eases maintenance on API integration, check out my blog on Understanding Digital Twins for Smart Building and Industry