<h2>Minds over machines</h2>
Seventy-three years ago, Alan Turing assessed a machine’s capacity to demonstrate intelligent behavior, a notion reflective of modern artificial intelligence. Jumping ahead to 2023, Modern AI systems are revolutionizing the way we approach tasks, make decisions, and interact with technology, ushering in an era where the boundaries between human capabilities and artificial intelligence continue to blur.
<h2>What is Artificial Intelligence?</h2>
AI is defined as the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
As we explore the integration of AI into our daily lives, we can observe its application in email services, where predictive text prompts streamline sentence completion through a simple swipe.
But what is so revolutionary about AI, and how has it progressed over the last century up to present time?
<h2>A Brief History of Artificial Intelligence</h2>
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<h2>Artificial Intelligence in AEC: Advancements in the Industry</h2>
The AEC sector, an embodiment of the teamwork of architects, engineers and construction professionals, plays a crucial role in both local and global economies, contributing an average of 6% to the global GDP.
In a 2018 McKinsey &amp; Company assessment of AI adoption across 13 industries, construction ranked among the bottom three for current AI use. Despite this, a study published in May 2023 highlights a rapid evolution in the AEC tech industry, characterized by heightened investments and a surge in startup numbers. Around $50 billion flowed into AEC tech from 2020 to 2022, marking an 85 percent increase compared to the previous three years.
<h2>A brief history on Technology in the AEC industry</h2>
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Undoubtedly, AI is significantly transforming various industries. Experiencing a notable rise in technology adoption over the past three years, the AEC industry has seen the influence of this trend spreading across various areas:
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Finally, buildings are evolving beyond mere walls, roofs, and masonry. With the integration of Internet of Things (IoT) devices, sensors, and automation systems, such as Para’s digital twin and building analytics platform, buildings are becoming intelligent structures. However, “intelligence” stems from data analytics, as data without analytics is comparable to a puzzle without assembling—a collection of pieces waiting to reveal the complete picture.
Here are some of the transformative ways AI has transformed the AEC industry.
<h2>Unleashing Reinforcement Learning for Optimal Energy Solutions</h2>
The prevailing energy consumption optimization approach is primarily reactive, responding to unfolding events without proactive planning. Using tools like Reinforcement Learning (RL) becomes crucial to transition to a more strategic stance.
RL is a branch of AI concerned with the decision-making process. Likened to playing chess against a computer, the agent interacts with the environment (chessboard and PC), with each action resulting in different states and earning rewards. RL aims to maximize rewards by guiding the agent to learn optimal steps for efficient decision-making.
In the context of the AEC industry, specifically for energy consumption optimization, RL is being explored to enhance processes such as scheduling HVAC systems.
Taking the example of HVAC optimization, the digital facility manager, acting as the agent, controls the Building Management System (BMS) impacting the HVAC zone—the designated environment. Unlike electrical systems, HVAC systems require advance planning, and hasty reactions may have detrimental consequences due to the dynamic nature of the environment. To mitigate risks, a digital twin of the environment is created, allowing the RL agent to learn and optimize without immediate real-world consequences.
The effectiveness of RL depends on the user. While one may possess extensive knowledge in this field, it is essential to apply it judiciously, avoiding trivial scenarios like the chess example. Recognizing the strategic use of these skills is crucial, reflecting the forward-thinking approach embraced by Para.
<h2>Fault Detection &amp; Diagnosis: Mitigated Downtimes &amp; Increased Savings</h2>
Mitigating unscheduled downtime is crucial to reducing operational expenditure, extending machinery lifespan, and minimizing costs and risks. However, pre-defined engineering rule-based solutions have limitations in detecting faults not previously accounted for by these rules.
To address these limitations, machine learning models can be used to learn the complex relationship between the inputs by using the machinery&#8217;s historical data. Two paradigms of machine learning &#8211; Supervised and Unsupervised learning &#8211; can be used to refine the model by minimizing deviations between actual and predicted values, or by learning the underlying relationships between inputs. Any deviation in the machinery&#8217;s behavior can be detected, and these deviations can be investigated by subject matter experts to prevent major issues.
During the investigation phase, AI, particularly clustering, proves highly beneficial in categorizing faults with similar patterns. Ultimately, by linking the outputs of multiple AI systems together, an advanced classification system that predicts the fault label associated with the root cause of the issue can be achieved.
Incorporating AI into the fault detection system ensures proactive maintenance, minimizing downtime, and reducing operational costs.
<h2>The importance of AI applications in bringing about positive changes.</h2>
The wealth of available data surpasses the realms of IoT and building systems, and more extensive source lies elsewhere. From blueprints to material specs, there’s a digital record for almost everything. All types of data sources are serving as the driving force for numerous emerging AI applications:
AI-powered tools can analyze vast amounts of data and generate intelligent design suggestions, thus optimizing the design and planning processes, ultimately resulting in more efficient and sustainable structures.
LLM-powered tools are on the rise, and engineering tools infused with LLM capabilities are beginning to emerge, empowering users to access information, query databases and offering numerous functionalities beyond our current imagination such as converting text prompts to concept designs.
AI and sensor-enabled wearables hold the promise of revolutionizing the construction industry, offering workers instantaneous information about their environment and their well-being (i.e. fatigue and stress monitoring), providing them enhanced safety and productivity.
AI-powered drones and robots are set to revolutionize the AEC industry, bringing enhanced efficiency to site surveying, inspections and construction operations. The synergy of drones, robots, and AI, provides construction automation thus enhancing productivity, ensuring safe operation in high-risk areas, and meeting deadlines.
<h3>The future of AI in AEC</h3>
With the expanding use of AI-powered tools, the AEC sector&#8217;s productivity surge, estimated by McKinsey, could translate into a market value of approximately $1.6 trillion. The rapid adoption of AI brings challenges, such as addressing data privacy, maintaining ethical practices, and overcoming expertise shortages. Prioritizing workforce upskilling and reskilling, along with developing applications that balance customer experience and workflow execution, are essential.
Ultimately, in the face of challenges such as climate change and urbanization, AI emerges as a transformative force, fostering efficiency, sustainability, innovation, and safety.

The Commanding Presence of Artificial Intelligence and Digital Twin Technology in AEC

The Digital Twin concept is experiencing explosive growth across various industries and has a unique value proposition in the realm of buildings. Together with the Building Management Systems (BMS), the Digital Twin brings forth many benefits that simplify the life of an operator or building manager. This blog will clearly outline the enhancements that intelligent platforms like Digital Twins can add to the BMS, showing how these technologies work hand in hand to elevate building performance and efficiency.
<h2>What is a BMS? 
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A Building Management System (BMS) or Building Automation System (BAS) is a system installed within buildings to monitor and regulate the various building systems. BMS technology and its connectivity extends across all types, sizes, and functions of facilities. Ultimately, a BMS acts as a central hub, gathering data from sensors and devices throughout the building and using it to automate functions, control devices and send alerts. In the last decade the BMS has evolved to Integrated Building Management System (IBMS), from being just a standalone application system to an integrated one by integrating with all possible data source from different applications.
The BMS operates in three layers: the management layer (layer 1) contains the human interface and acts as data management, storing historical and real-time data. This layer differentiates the BMS from the IBMS where data from different applications are integrated. The automation layer (layer 2) provides primary communication and control, consisting of hardware and software such as automation and supervisor controllers, field controllers, and built-in software applications, which bridge the field layer and management layer. Finally, the field layer (layer 3) consists of physical sensors, actuators, and application-specific controllers directly connected to equipment for mentoring and controlling equipment, which continuously measures and monitors physical parameters such as temperature, pressure, and humidity.
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What is a Digital Twin?
A digital twin is a digital mirror of a physical asset or building. The journey of a digital twin begins far earlier than its physical counterpart and starts during the design process. A digital twin forms a common data environment for both the design phase and operation phase, achieved through the integration of various data sources such as Building Information Model (BIM), design simulation, construction data, warranty &amp; maintenance, information technology, operating technology, historical and real-time data from different Building Management Systems such as HVAC, lighting, physical security, electrical, utility meters, life safety, communications, other external applications and computerized maintenance management system – to name a few. Below are the different degrees of digital twin sophistication and the current maturity of digital twins.
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Each type of digital twin has its own purpose and complexity. It’s always recommended to consult a Digital Twin specialist to identify which type of twin is better suited for the expected outcomes. 
<h2>How do the Digital Twin and BMS work hand in hand to improve building performance and efficiency?  </h2>
<h3>Platform / Application Capabilities </h3>
The BMS industry has made massive progress at all three fundamental layers and has come a long way to enhance its management layer to suit multiple integrations. Nevertheless, a significant gap exists in contextualizing integrated data to improve building efficiency. At a core level, the BMS application is purpose-built to cater to specific building operations or functions, and altering its purpose-built infrastructure to churn these integrated data to enhance the building operations and performance is still a big challenge. A Digital Twin platform can bridge this gap. The primary purpose of a Digital Twin platform is to efficiently handle a big data environment (i.e., enormous amounts of data generated during the entire building life cycle) and establish the proper context and relationship among the data. Thus, the Digital Twin platform acts as a building operating system that holds the entire building life cycle data in a proper context for a single pane of glass, monitoring and working as a common ground for advanced analytics and AI/ ML to improve building efficiency and day-to-day operations. For instance, The Digital Twin platform, with the help of advanced analytics, can identify inefficient systems not just at a single application system level but at the entire building level in a Unified User Interface (i.e., a single interface for multiple applications within a building), which will significantly help to optimize the maintenance process and allow the user to make an informed decision on the required capital expenditure to improve building operations.
<h3>Improved Building Operations: </h3>
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<li>Seamless knowledge transfer– The BMS application may have limitations in holding the complete lifecycle data of an asset, system, or building. Even if an existing document repository is used, it may not be linked to an asset. Digital Twins hold the entire lifecycle data of an asset, such as location, date of installation, design data, manufacturer details, maintenance details, document repository, historical and real-time data, and calculated data. This will help the user document the information in a structured format and have it readily available for another user with minimal knowledge about the building. So, when a maintenance team arrives at a facility to handle a work order, instead of going through multiple documents and applications to identify the location, design and operational details of an asset before stating maintenance work, these details are readily available to the user in the Digital Twin platform, significantly reducing maintenance duration.</li>
<li>Enabling remote operation– The current infrastructure of a BMS application allows remote monitoring, but it requires a competent person to handle each specific application. The Unified User Interface and standardized alarm/ insight management features of Digital Twins allow the user to monitor the entire building operation through one single platform. For example, a typical Facilities Management (FM) team needs a BMS specialist, physical security specialist, and EMS specialist to view three separate applications. Using Digital Twin software, however, they can monitor all data simultaneously and access specific applications to make critical interventions. Thus, enabling seamless remote monitoring and operation of the entire building.</li>
<li>Facilitating intra-system operation– Digital Twin allows bi-directional data flow between different systems for improved operation, which may not be possible with all BMS applications. For example, when an employee enters the building, the data from the access control system can be used in the HVAC system, lighting system, and kitchen system to adapt the office space for the employee’s arrival.</li>
<li>Reactive to predictive maintenance process transition– The data from BMS applications are used for reactive maintenance. The Digital Twin platform, powered with advanced analytics and AI/ ML models, can analyze vast volumes of data from different applications and detect abnormal behavior or consumption patterns to predict potential failures and critical operations. The prediction results combined with real-time/ near real-time recommendations can streamline the entire maintenance process, reduce equipment downtime, and improve the asset operation.  For example, the Digital Twin platform can automatically identify abnormal solar panel power generation patterns and benchmark them against solar power availability to schedule a maintenance task in the CMMS system. This transition from a reactive to a predictive approach streamlines the maintenance process by regulating maintenance and improves solar panel power generation efficiency.</li>
<li>Simulation and Forecasting – BMS applications may pose a challenge while feeding the data to the simulation engine or directly incorporating the simulation environment within its existing infrastructure. But it can be quickly addressed using the Digital Twin platform as it holds all required design (static) and operational (dynamic) data and uses building physics, simulation tools, and AI / ML models to simulate real-time energy consumption targets or future state what-if scenarios. This allows the user to better understand how the building&#8217;s energy consumption pattern changes with occupancy. The simulation and forecasting feature is a unique value proposition of the Digital Twin platform.</li>
<li>Intelligent Insight Management and Alarm Remediation– The BMS application issues alarms and warnings at an asset level, resulting in high alarms, false triggers and duplicate warnings. The Digital Twin, coupled with the Fault Detection and Diagnostic (FDD) engine, analyses these alarms and warnings at an entire asset ecosystem level to diagnose and identify the root cause and prescribe required recommendations to alleviate these issues. For example, an AHU can’t maintain the supply air temperature at its setpoint and is serving fifty VAV boxes &#8211; in the BMS, it will generate an alarm at AHU and fifty VAV, but the Digital Twin will analyze these alarms to identify the exact root cause, provide actionable data-driven insight to mitigate the problem, and suppress false alarms at fifty VAV. This feature is a considerable help for the facility management team to handle alarms or faults efficiently.</li>
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<h2>A more intelligent building </h2>
The integration of Digital Twins and Building Management Systems (BMS) brings numerous benefits that can improve building performance and efficiency. The Digital Twin platform acts as a building operating system that efficiently handles big data and establishes the proper context and relationship among the data. This allows for advanced analytics and AI/ ML to optimize building operations and maintenance processes, leading to informed decision-making and improved building efficiency. The seamless interaction between the BMS and Digital Twin platform further enhances the building&#8217;s lifecycle data, allowing for a unified user interface and holistic view of the building&#8217;s operations. As such, the integration of the Digital Twin and BMS is a promising solution that can simplify the life of the operator or building manager and elevate building performance and efficiency.
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How do Digital Twins and the BMS work hand in hand to improve building performance and efficiency?

In the rapidly evolving landscape of smart buildings, where advancements in cloud computing, artificial intelligence, and the Internet of Things (IoT) are reshaping our interaction with physical spaces, the concept of the Digital Twin stands out as a pivotal innovation. A digital twin serves as a dynamic, digital replica of a physical asset, system, or process, facilitating insights and decision-making that are grounded in the virtual reflection of the real world. But to fully unlock its potential, especially for buildings at the design stage or those lacking the right infrastructure, you need a well-thought-out Digital Twin Readiness Strategy. This strategy refers to the preparation and planning required to effectively implement a digital twin platform for a facility or group of facilities. It is a comprehensive plan that assesses and prepares the existing infrastructure of a building, or the planned infrastructure of new constructions, to seamlessly integrate with digital twin technology.
In this article, we&#8217;ll delve into what this strategy entails and why it&#8217;s essential for your future-proofing efforts.
What is a Digital Twin Readiness Strategy?
Before we jump into the &#8220;why&#8221; and &#8220;how&#8221; of a Digital Twin Readiness Strategy (DTRS), let&#8217;s establish what it is and why it matters.
Imagine building a house without a solid foundation. It might look good initially, but over time, it&#8217;s bound to crumble. Similarly, for digital twins, the DTRS is not just about setting the initial stage; it&#8217;s about ensuring that the foundational infrastructure for your digital twin platform is robust and capable. It goes beyond mere preparation – it actively establishes the necessary &#8216;enabling&#8217; infrastructure, which streamlines and simplifies the deployment process. This proactive approach anticipates potential challenges and addresses them beforehand, facilitating a smoother and more efficient implementation of the digital twin platform.
Think of it as the prelude to the digital twin solution. It&#8217;s the preparation phase that ensures everything is in place for a smooth implementation. You wouldn&#8217;t want to start a performance without a rehearsal, right? Similarly, a DTRS prepares you for the real show. It ensures you get the most out of your digital twin investment. It helps you extract valuable insights, optimize operations, and make data-driven decisions that can revolutionize your building management.
Why is a Digital Twin Readiness Strategy needed?
Understanding the necessity of a Digital Twin Readiness Strategy requires recognizing the unique challenges and opportunities presented by both existing and new buildings:
For an existing building, the transition to a digital twin is not a mere upgrade—it&#8217;s a strategic evolution. A Digital Twin Readiness Strategy is indispensable as it encompasses a thorough assessment of the building&#8217;s current state. This evaluation is critical to ascertain the compatibility and readiness of the built environment for the adoption of a digital twin platform. The strategy meticulously examines the infrastructure to determine the extent to which digital twin functionalities can be integrated and identifies the scope for potential operational improvements and retrofits. It&#8217;s a detailed process that seeks to align the building&#8217;s current capabilities with futuristic digital twin solutions, ensuring a smooth transition that brings about enhanced efficiency and performance.
When it comes to new constructions, a Digital Twin Readiness Strategy is equally vital. The strategy is instrumental in laying the foundation for the seamless integration of digital twin technologies right from the design phase. It involves the development of a comprehensive plan that clearly outlines the building owner or manager&#8217;s objectives. This includes bringing together various stakeholders—architects, engineers, IT specialists, and end-users—early in the process to collaboratively shape the final solution. This collaborative approach ensures that the digital twin aligns with the building&#8217;s intended function and the stakeholders&#8217; expectations, establishing a foundation for the building&#8217;s long-term relevance and efficiency. The strategy is also aimed at extracting the maximum value from the digital twin by tailoring the building&#8217;s design to accommodate the specific use cases that the digital twin will support. A pertinent example is aligning the building services zoning, particularly HVAC distribution &amp; zoning and lighting circuits &amp; controls, with the intended function of each space. This alignment is crucial for maximizing energy savings through occupancy-driven adaptive controls. By doing so, the DTRS strategy ensures that the infrastructure is not just digital twin compatible but also optimized to take full advantage of the digital twin&#8217;s capabilities from the outset. The readiness strategy in this context is about foresight and planning—the meticulous orchestration of design elements to accommodate advanced technologies, thereby guaranteeing the building&#8217;s relevance and efficacy in the digital future.
When is a Digital Twin Readiness Strategy required?
When it comes to implementing a Digital Twin Readiness Strategy (DTRS), timing is crucial. To fully leverage the benefits of a digital twin, it&#8217;s advisable to start early, particularly during the design phase of new buildings. This strategic approach ensures the establishment of a robust foundation essential for supporting the comprehensive architecture of the system. In the context of digital twins, a DTRS at the design stage sets the stage for the digital twin&#8217;s success.
For existing structures, the period before the deployment of a digital twin platform is the opportune moment to adopt a Digital Twin . This strategic evaluation is comparable to conducting a thorough audit of a building&#8217;s capabilities, determining feasible modules and use cases based on current infrastructure and data. It also serves to uncover data gaps, and addressing these can unlock further possibilities for use cases and modules, thus enhancing the building&#8217;s digital twin capabilities with more actionable insights and opportunities.
This approach ensures that you&#8217;re well-prepared and strategically aligned for the successful implementation of digital twin technology, whether it&#8217;s in the context of new construction or existing structures.
Digital Twin Strategy in practice
Understanding the mechanics of a Digital Twin Readiness Strategy reveals a two-pronged approach.
For existing buildings, the process commences with a meticulous evaluation of each system&#8217;s current state, focusing on data availability and generation capabilities. Enhancements are recommended where needed, and a readiness scoring system helps benchmark the building&#8217;s current state against digital twin viability, effectively providing a clear roadmap for potential upgrades and improvements.
For new buildings, the DTRS involves the application of Digital Twin Ready Design guidelines from the outset. During the design phase, these principles are integrated into the engineering design drawings and documents, and the Building Information Model (BIM) is prepared to cater for a digital twin platform. This means that the relevant model parameters are set up to accommodate future integration with digital twin technology.
Addressing the concern of obsolescence due to rapid technological advancements, the DTRS includes a forward-thinking approach. It involves not only configuring current technologies but also allowing for flexibility and scalability in the design. This adaptability ensures that as new technologies emerge, the building infrastructure can be updated or expanded with minimal disruption. For building owners and managers, staying informed about technological trends and periodically reassessing the digital twin strategy is crucial. Regular updates and the ability to integrate new technological advancements can help mitigate the risk of the DTRS becoming outdated. This proactive and flexible approach ensures that buildings remain relevant and efficient in the face of rapidly evolving technology.
How a Digital Twin Readiness Strategy Paves the Way for Intelligent Buildings
The strategy conducts a thorough evaluation for existing buildings and ensures proactive integration during the design phase for new buildings:
From the data availability perspective, the strategy assesses the current infrastructure for existing structures to determine if the necessary components for data capture are in place. For new buildings, it ensures that the design incorporates the necessary software, systems, controllers, and sensors from the start, laying a solid foundation for future intelligence.
From the data granularity perspective, the strategy identifies opportunities in established buildings to introduce additional sensors to enhance diagnostics and precision. In the blueprint phase of new constructions, it advises on the strategic placement of sensors to ensure rich, detailed data collection upon completion.
From the data integration perspective, the readiness strategy aims to integrate isolated data streams in existing buildings, promoting a seamless flow of information. For new developments, it advocates for the design to facilitate data interoperability and a centralized data environment from the initial stages of the project.
From the BIM perspective, when applied to current buildings, the strategy includes evaluating and, if necessary, enriching the BIM aspects to provide a comprehensive digital representation. For new projects, it ensures that the BIM is fully integrated and digital twin-ready, with detailed asset parameters set from the initial design stages.
Paving the way for future intelligence 
In conclusion, a Digital Twin Readiness Strategy is an essential navigator in the realm of intelligent buildings, facilitating not only digital twinning but also the broader spectrum of Smart Building Software, such as Integrated Workplace Management Systems (iWMS), Energy Information Systems (EIS), and Fault Detection and Diagnostics (FDD) platforms. It guarantees the establishment of a suitable infrastructure, optimizing the digital twin&#8217;s operation.
More than just a setup, it&#8217;s a strategic approach to safeguard your investments against obsolescence and to minimize future expenditure. It positions your building to become a dynamic asset, enhancing value, efficiency, and providing deep insights.
By adopting a Digital Twin Readiness Strategy, you&#8217;re not just building for today—you&#8217;re investing in a smarter, more adaptable future.

How a Digital Twin Readiness Strategy can pave the way for intelligent buildings

In building and infrastructure systems, one of the greatest challenges for operators and managers is ensuring that their high-value, time-sensitive, and mission-critical assets are consistently operating efficiently and at the best possible capacity. Every single second of downtime can come with associated costs. Even an outwardly unremarkable drop in efficiency can have consequences in terms of energy consumption, costs, and lost productivity.
By leveraging the extensive capabilities of machine learning, Digital Twins with advanced machine learning algorithms can give operators a portfolio management solution to enable immediate fault detection and facilitate predictive maintenance in their mission-critical assets.
What is the difference between preventative and predictive maintenance?
Preventative maintenance is conducted at fixed intervals, regardless of existing conditions. This type of maintenance is carried about based on manufacturers recommendations. On the other hand, predictive maintenance is conducted whenever a need arises, with data-backed interferences to determine impending faults or failures. Predictive maintenance is also optimized to suit existing operating conditions within a facility.
Because predictive maintenance is performed only when needed, it can significantly reduce labor, operational and material costs.
How does predictive maintenance work?
By incorporating a number of strategically-selected machine learning algorithms, tailored to the product and tiered to cover every possible outcome, Digital Twins can drive predictive maintenance and empower operators to deliver the best value.
Take the case of an oil and gas pipeline. Naturally, any solid design for such an asset would include redundant systems that prevent a single point of failure from wrecking the entire system. Still, it’s important for operators to have clear insights into the performance of this pipeline – from its pumps to wear-and-tear on its bearings or a misalignment in an axle or shaft. The cost of downtime for such an asset could be devastating. Knowing immediately if there are faults affecting the behaviour of this asset or – better yet – being able to predict when a fault or failure is likely to happen can add enormous value to operators.
The signal is in the variation
One of the key components of a pipeline are its pumps, and therefore, monitoring pumps is widely recognized as crucial in maintaining pipeline operations. Traditionally, maintenance workers have relied on either manual inspections or regular preventive maintenance recommended by manufacturers. Today, however, with the rise of Internet of Things (IoT) devices and machine learning, it becomes possible for a platform like Para to give operators a real-time view of a given asset’s performance and integrity as well as advance warnings on any imminent faults or failures.
How does that work in practice? Vibration sensors attached to the pump would transmit data to the Digital Twin platform. The platform would include a tiered approach with machine learning algorithms that are tailored for the type of asset and for the specific goal or outcome as prioritized by the operator. For a pump, these machine learning algorithms can understand vibration sensed patterns and are able to detect which of these vibration patterns correspond to impending failures. Moreover, these algorithms are not only able to use vibration patterns to predict failure ahead of time, they can also be used to infer where a fault may be exactly. Different vibration patterns are associated with different failures or faults, and therefore, these patterns may be used to determine if an imminent failure is associated with, for example, bearings or a misalignment in axle and shaft.
Predictive maintenance, for all assets
A pipeline, of course, is simply one illustrative example. Digital Twins can be used for predictive maintenance in a wide variety of use cases. In buildings, for example, the HVAC system – one of the greatest energy consumers in a building – can be monitored and optimized in real time and down to its individual parts (such as air-handling units and other components). Similarly, a photovoltaic panel system can significantly reduce the energy consumption of a building, and a fault or disconnection in the system would imply a significant cost difference, not to mention a difference in carbon emissions. Predictive maintenance for such a system would save on costs, maintenance, and sustainability metrics.
The added value that Para offers all of these systems and various others – beyond regular maintenance and facility management practices – is that our platform uses machine learning to understand a building’s actual existing conditions and occupancy patterns. The platform builds on this understanding to tailor industry-standard equipment handling measures and specifications to accurately measure how machines are performing and implement recommendations accordingly. This way, we can leverage industry expertise and detailed insights into existing conditions to optimize systems and ensure smooth, sustainable, and highly resilient operations.

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How machine learning algorithms are paving the way for predictive maintenance in the built environment

Research around the world has consistently identified one extreme energy consumer: HVAC systems. On a country-wide level, HVAC systems consume between 30-40% of energy requirements, and in a given building, it’s not uncommon for the HVAC system to singlehandedly account for up to 70% of total energy consumption.
It is no surprise, then, that green energy movements around the world have prioritized reducing the energy consumption of HVAC systems.
Predictive maintenance – for when faults or failures may be too costly
Predicting faults or failures before they happen is a key component of a successful operation and maintenance schedule. Any fault or failure in the HVAC system – whether major or minor – will have a (sometimes disproportionately large) cost effect on operations. Therefore, detecting a fault in the HVAC system early – or ideally predicting that it will happen before it actually does – will empower operators to ensure smooth operations across a building or portfolio of assets and reduce both operational costs and associated carbon emissions.
Tracking HVAC performance to facilitate predictive maintenance
An HVAC system is not a centralized system. Its component parts are distributed across the building. HVAC systems must respond to existing conditions, such as outdoor temperatures, the amount of daylighting in a given room, changing occupancy patterns, and so on. Therefore, monitoring the performance of an HVAC system and optimizing performance is quite a complex task.
Digital twin solutions like Para, with advanced machine learning capabilities and artificial intelligence algorithms – can facilitate this process, making it easier to detect potential faults before they occur and to optimize performance.
<ul>
<li>Capture and learn the behaviour of the system: By gathering and compiling data from a given system, operators using Digital Twin Solutions can employ machine learning and AI algorithms to detect that machine’s patterns of behaviour. Best of all, the platform can begin to consider what the HVAC system’s demand would be for any given occupancy level, at a given outdoor temperature, and at any other operating condition. By collecting all of this data, the platform captures the behaviour of the system and represents it in a digital twin. It then becomes possible for the platform to paint an accurate picture of what the actual HVAC consumption should be for a given set of conditions. That enables the next critical step of the process.</li>
<li>Watch for the variation: Once the predicted behaviour is established, it becomes far easier to detect faults. In fact, any deviation between the predicted behaviour and the actual behaviour of the system becomes an indication of a possible fault. If the deviation is particularly remarkable or consistent across a given time period, operators will be alerted and empowered to take action.</li>
<li>Trigger fault detection and diagnosis checks: Once operators detect that variation, the next step is to detect and diagnose the fault. For an HVAC system connected to a Digital Twin, a variation can trigger another series of algorithms that can run automated checks to attempt to diagnose the existing fault. The fault detection and diagnosis (FDD) checks embedded in Para depend on industry standards such as ASHRAE’s set of FDD checks and algorithms. These checks are triggered and constantly running, checking the health and integrity of sensors and components in HVAC systems. The algorithms can check, for example, the filter check status coming in from the building management system, the particular maintenance history of a given component, any available and relevant camera data, and even whether there is wear-and-tear on that component. The more data the system can acquire, the more accurate the model will be at detecting potential faults.</li>
<li>Issue recommendations in a work order: Once a deviation is tracked, the Para platform can issue a recommendation for maintenance or further checks, if the fault itself was not accurately diagnosed. By doing so, the platform empowers operators to track deviations before costs snowball.</li>
</ul>
The case of the clogged AHU
In one application, Para’s team developed and implemented a machine learning algorithm to track and learn the behaviour of an air handling unit. During a stage in operations, the platform tracked an outward deviation between the actual and the predicted behaviour and alerted operators to that variation. When attempting to diagnose the issue, operators noticed that a single filter in the supply fan was clogged. Once it was cleaned and the sensors once again gave a clean signal, energy consumption in the building over the next week was reduced by 5 to 10%. With other energy optimization measures, the team were able to achieve a drop of 20 to 25% in energy consumption – leading to significant savings in both costs and emissions.
Once applied to an entire building or campus or facility, Digital Twins can use their predictive maintenance and real-time monitoring capabilities to immediately identify cost-saving opportunities, detect and diagnose faults early, and implement measures that optimize energy performance and prolong the lifetime of key building equipment.

Predictive maintenance for HVAC systems – the ultimate endgame for building energy management

It happens every time. A developer and a team of designers and engineers put together a building and estimate that that building will consume a certain amount of energy. Plans and projections are made accordingly. Then, the building is constructed and open for business. Soon afterward, it quickly becomes clear that that building is using far more energy than anyone had predicted. And we’re not talking about tiny percentages or a few kilowatts here and there. We’re talking about an average standard of twice the energy amount that was predicted in the original design. Even buildings that were certified LEED Platinum for energy efficiency in the design stage often end up consuming far more energy than anything predicted in models.
So why the extra energy? 
Many building have an energy performance gap – defined as the difference between (A) the energy consumption that was forecasted in the building’s design stage and (B) the energy use measured during actual operations. A significant amount of this extra energy can be reasonably explained in terms of building use and operations, but recent research suggests that as much as 30% of the energy used in commercial buildings is actually going to waste.
Tracking energy waste – an advantage for buildings and planet alike. 
Micro-scale benefits 
Identifying energy gaps and energy inefficiencies and implementing actionable intelligence to plug these gaps can boost performance by up to 30%, potentially resulting in hundreds of thousands of dollars in savings.
Macro-scale benefits 
Buildings are responsible for 38% of global carbon emissions, and over 85% of those emissions will be emitted during the operation and maintenance phase of a building’s lifecycle. 30% of those emissions are rooted in energy waste. If we could make a dent in reducing this energy waste, we would really be moving the needle in global carbon emissions.
That energy waste stems from a variety of different factors, including three particularly prominent culprits.
<ol>
<li>The one from day one – A building’s passive performance gap: Façades, building orientation, and HVAC equipment are just a few examples of static, intrinsic building components that have a direct and even predominant impact on a given building’s baseline energy performance and building operations. A passive performance gap is often a construction-related intrinsic issue, such as, for example, fit-out choices that could have compromised performance. This gap would exist from day one, exerting significant impact on energy waste.</li>
<li>The deviant one – A building’s active factor waste gap: Active factors in energy waste are deficiencies and irregularities that result from direct deviations from maintenance plans or, alternatively, from the lack of adaptive maintenance. Such factors for example could include delayed replacement of air-handling unit filters, inefficient or irregular cleaning of photovoltaic panels, or simply regular wear-and-tear and aging issues.</li>
<li>The ever-changing one – A building’s response to changing operational conditions: Operational factors in energy waste are factors that arise from deviations to the standard sequence of operation or the standard operating procedures described for equipment, or assets, or process. Moreover, there are other external varying factors. For example, a certain room may have an occupancy of 20 people, then 10, then 4, then 20 again. If heating or cooling devices, for example, cannot respond automatically to these changes, energy would be wasted.</li>
</ol>
As a building analytics and comprehensive building portfolio management platform, PARA makes it easier for building owners and operators to track energy use and identify and trace wasted energy. Most building platforms provide an actual energy usage intensity figure. PARA goes beyond that by disentangling and separating passive, active, and operational waste, to create better and more tailored recommendations for addressing energy waste and achieving better energy efficiency.

Tracking three common culprits in energy waste

It’s the age of data – here’s how to make data work for your building portfolio 
Data has completely revolutionized managing building operations – making it easier to pursue energy efficiency, detect leaks and faults before they cause major issues, and create adaptable spaces that offer enhanced user experiences.
Buildings have always created enormous amounts of data – but historically, such data was difficult to collect. Even when data is collected, different datasets have often remained in silos. Information on the maintenance and performance of a corporate office’s HVAC system, for example, will sit comfortably in the drawer of the maintenance team while essential connected information – such as the occupancy patterns in spaces that use HVAC significantly – remains locked up with the human resources department.
Disconnected in such a way, these datasets are left untapped and unexplored – an enormous lost opportunity.
Essential data, from everywhere and in real time
Para connects to and integrates data from different sources and in different technical languages on one platform. All that data is converted into one common language and set upon by the platform’s advanced machine learning and analytic capabilities. The result? Building operators are empowered to connect extensive datasets on multiple building systems and then mine those for insights that improve collective performance.
And what exactly does all of that mean, in actual practice?
Begin with why
To determine how data can be used most effectively, Para’s team coordinates with clients and their various stakeholders to determine their needs and aspirations for their building, portfolio or asset. Depending on priorities, Para’s team will examine existing data sources, identify gaps, and recommend measures that are tailored to achieving a client’s specific objectives.
Recognize the value of legacy and existing systems 
Drawing data from existing systems has a number of benefits. Para connects to a given building’s legacy systems or existing conventional low-current systems – such as building management systems, lighting controls, access controls, existing meters, and any similar devices – which have databases of historical information on the asset. Leveraging existing and legacy systems also reduces capital costs or additional investments needed to operate the building management platform at optimal value.
Introduce IoT – sparingly and effectively 
Depending on the needs of operators and clients, Para’s team may identify data gaps and recommend the installation of cost-effective devices such as IoT (Internet of Things) sensors or IoT actuators, which could be extremely valuable data sources. As a vendor-agnostic operator, our company only recommends products and devices that could help our clients achieve their objectives. Moreover, these recommendations are sparing, focusing only on additions that can provide significant returns on investment. With these extra devices specified as needed, Para’s team will be able to plug in any existing gaps in data needed to meet client objectives.
Leverage today’s data-ready equipment 
In today’s world, many assets, equipment, and building services are IoT-ready. Home appliances, smart TVs, AC units, and even some types of furniture can now transmit data and can be connected and managed via special applications. As a highly adaptable building management platform, Para is capable of connecting to such systems and tap into their data streams to provide a more granular and detailed understanding of building operations.
Take advantage of online databases
The amount of essential data that is available online is also awe-inspiring. Para can connect to relevant online databases and datasets in order to optimize operations and give operators a better understanding of how their asset compares to others in similar categories. For example, Para can leverage online databases to analyse maintenance faults in certain systems by benchmarking against the Mean Time Between Failure (MTBF) of similar systems and components, or other similar metrics that are available online. Elsewhere, Para can also use weather forecasts to optimally load and unload cooling and heating plants. Tapping into online resources can boost the value of the insights Para can offer.
Unleash AI and machine learning to obtain actionable insights  
As the ultimate building management platform, Para can receive these inputs from all building systems, in real time and in various formats. The platform’s seamless and agnostic architecture allows the numerous disparate data sources to be converted into one common language. That converted data is then analysed by Para’s powerful proprietary machine learning and artificial intelligence (AI) engine to produce predictive, proactive 24/7 insights that empower operators to meet the objectives set for their assets.
Visualize data for everyone 
Data can never truly meet its full potential unless it’s set in a digestible, clean format that is easy to understand and act on. For this reason, Para provides a dashboard that is a single source of information tailored and customized to the needs and requirements of different operators. The platform’s digital twin feature is an exceptional visualization technique that can provide a full picture and be customized to the specific requirements of any given client stakeholder (from maintenance and security to human resources), enabling maximum immediate and long-term value.
Track outcomes  
Para not only offers recommendations for improving performance, it also allows operators to model the performance of these recommendations before they are implemented. In turn, this builds confidence in expected outcomes and provides a foundation for intelligent decision-making. Moreover, once recommendations are implemented, Para can be used to monitor improvements and demonstrate progress.

Insights