Upping the game of predictive analytics
Martin Huber, co-Founder and CEO of Amrax, looks to the increasing role of AI's predictive analytics in helping to improve long-term building health and performance.
As urbanisation, digitalisation and climate change continue to fuel a new age of sustainable infrastructure, building health and performance is now more in focus than ever.
As the net zero target gains greater sense of urgency, it is becoming increasingly important that our buildings are designed and operated in ways that optimise and minimise environmental impact. This, of course, becomes even more important in the wake of continued energy price instability, especially for energy-intensive and commercial operators who have been forced to accelerate green measures in order to build resilience and safeguard against further energy price hikes.
But it’s also about building health. Amid our post-pandemic era of health awareness and better understanding of mental health, there is a much greater emphasis on creating indoor environments and workplaces that cater to the wellbeing of occupants.
The result is a complex challenge for the modern building operator, especially those responsible for overseeing vast industrial and commercial establishments. However, the good news is that there are solutions at hand to help, such as the latest generation of 3D virtualisation technology.
Of course, most building operators will already be well acquainted with the 3D visualisation concept. For years now Building Information Modelling (BIM) models have had a transformative effect on commercial construction projects, allowing designers to detect errors before construction begins, avoiding costly changes further down the line.
However, while much of the focus surrounding virtualisation via BIM appears to centre on the inception stages of a build, it also offers even greater scope to the overall performance and health of a building.
Improved maintenance and diagnostics
Take, for example, an ageing factory building. When AI predictive analytics are applied to a 3D model of the site it becomes possible to forecast potential structural issues in advance. This proactive approach allows building operators to schedule structural work and maintenance before serious issues occur, minimising downtime and avoiding expensive repairs.
Furthermore, by leveraging predictive analytics, building operators can identify the root cause of any structural issues or strains and take remedial actions promptly. This data-driven approach also vastly reduced the amount of time spent on maintenance matters for resource efficient and improved costs.
Energy optimisation
Beyond maintenance, virtualisation supported with predictive analytics can play a huge role supporting ongoing sustainable strategy. For most building operators, of course, one of the biggest priorities at the moment will be reducing as much energy and emissions as possible without compromising the overall functionality of a building.
By using a 3D virtualisation platform in conjunction with predictive analytics, it becomes possible to gain a holistic and accurate overview of energy consumption patterns based on historical patterns, seasonality data, occupancy rates, production rates, and other relevant factors.
Through the power of analytics, building operators use these insights to make informed decisions on opportunities to optimise energy consumption. It may be, for example, that there is a new way to adjust HVAC systems based on occupancy patterns or an opportunity to introduce an intelligent lighting system.
This data-driven approach makes it much easier for building operators to build the business case for any green investments. Working digitally on a collaborative basis with other partners also reduces churn, time and budget spent correcting mistakes.
For vast commercial premises such as factories or manufacturing plants where even a moment’s downtime can incur significant revenue impact, this is essential to ensuring disruption is minimised and projects remain on schedule
Building health
Through the parametric data available from the 3D model, operators can gain a rich and accurate view of the key assets which play a role in ensuring a building’s health. This information allows architects, engineers, and designers to plan and modify building systems including electricity, HVAC and plumbing. The data storehouse also helps to manage the building utilities and chart out preventive maintenance.
Aligned to this, data into occupancy patterns and behaviour can help operators better understand usage needs and preferences. Couple this with predictive analytics to identify peak demand hours, and there is an opportunity to optimise temperature settings and lighting levels accordingly, thus negating unnecessary energy consumption during quiet periods and ensuring peak comfort levels are achieved.
A continued effort
In the operational phase of buildings, AI-driven 3D models can play a pivotal role too. These models can be used for space optimisation, energy management, and ensuring compliance with safety regulations. AI can process data from various sensors within a building to provide real-time insights, allowing for more efficient building management.
Take, for example, a large warehouse facility. AI-driven 3D models can optimise the layout for delivery placement and workflows, using data from foot traffic and inventory patterns. This can lead to increased efficiencies and improved customer experiences.
While the health and wellbeing of their employees and long-term sustainability goals may be the top priority in these efforts for employees, there are financial gains too.
Healthy Gains
Generally speaking, healthier buildings are more efficient ones, meaning most operators can expect to see significant energy cost savings as a result of building health improvements.
Visualisation tools make the process of creating this type of environment much more straightforward. Designers and building managers can see how their building is or will be used in practice and experiment with incremental improvements. This could be as simple as moving furniture away from walls to reduce the risk of mould and improve air flow, through to how different lighting layouts and solutions will create a more comfortable aesthetic.
By using 3D modelling, supplemented by building data analysed by AI, each environmental factor can be easily modified to find the optimum solution. This has minimal cost and could see outsized returns to building operators.
Smart buildings
Eventually, the majority of buildings will be embedded with smart devices and beacons that will monitor energy consumption and a range of other factors in real-time. When combined with AI automation and visualisation platforms, we’ll have an incredibly powerful set of tools to create ultra-efficient and highly responsive ‘living buildings' that will use considerably less energy and resources to maintain.
With AI, spatial data and 3D visualisation advancing hand-in-hand, the speed and precision of room and building design and ongoing maintenance are only going to accelerate. In the not-so-distant future, machine learning algorithms will be powerful enough to create design proposals with optimal efficiency.
In short, we are on the cusp of a digital revolution. This will be instrumental as challenges such as urbanisation, sustainability requirements and resource management play a critical role in combating climate change.
