The future of large-scale building management: AI-driven, multi-site systems

If you're a consistent follower of our blog series, you're likely already acquainted with the innovative wave of multi-site building management systems that are reshaping the industry. Today, we dive deeper into a pivotal facet of these systems, a technological revolution steadily gaining momentum: Artificial Intelligence (AI). This blog aims to unravel the complexities and potentials of AI, a tool not just evolving in capabilities but poised to redefine the pace and scope of advancements in building management. Join us as we explore how AI is transforming the landscape of multi-site operations and what this means for the future of the industry.
 

For decades, traditional building management systems have been at the core of operating and maintaining various building functions, including HVAC, lighting, security, and energy management. Historically, the oversight of these buildings was centralized, handled by dedicated employees for optimal management of the entire building portfolio. However, with the burgeoning growth of property portfolios and the increasing complexity of managing large-scale, multi-site operations, a significant shift is underway. Organizations are increasingly turning towards Artificial Intelligence (AI) not just for basic management, but also for enhancing energy efficiency and optimizing usage. This transition to AI-driven systems marks a pivotal change, offering smarter, more efficient, and cost-effective solutions for the challenges of modern building management.

Understanding Artificial Intelligence

Before diving into the intricate role of Artificial Intelligence (AI) in building management, let's first demystify this widely used yet often misunderstood term. At its core, AI is the replication of human intelligence processes by machines, especially computer systems. This technological marvel involves the creation of algorithms and sophisticated software designed to enable machines to perform tasks or make decisions that traditionally required human judgment and reasoning. It's about teaching computers to 'think' and 'learn', a concept that's revolutionizing not just building management but numerous industries across the globe.

Artificial Intelligence (AI) is adept at handling a myriad of tasks including problem-solving, learning, reasoning, understanding natural language, recognizing patterns, and decision-making. AI-driven systems are uniquely designed to not only analyze data but also adapt to new information and environments, enhancing their performance over time. Currently, AI is categorized into two main types:

• Narrow or Weak AI is designed for a specific task or a limited set of tasks. It is not capable of performing outside its predefined scope. Examples include virtual personal assistants like Siri or Alexa and image recognition software. You’re probably familiar with both.

• General or Strong AI refers to AI that possesses human-like intelligence and can perform a wide range of tasks at or beyond a human level. For now, it remains largely theoretical but it’s the subject of ongoing research and development.

 Another key concept in AI, particularly relevant to building management, is machine learning (ML). ML, along with subfields like natural language processing, computer vision, and robotics, plays a crucial role in the functionality of AI technologies. In the context of Building Management Systems (BMS), ML is indispensable. It involves training algorithms on extensive datasets to recognize patterns and make informed predictions or decisions, all without being explicitly programmed for each task. This ability for self-improvement and adaptation is what makes ML, and by extension AI, an invaluable asset in the evolving landscape of building management.

AI in BMS: turn the volume up to eleven!

Now that we’ve defined what it is let’s see how the application of AI to the facilities management industry should theoretically lead to innovations and improvements in various aspects of the building manager’s life and work. We can see how and why that’s the case by considering various scenarios. Here are eleven ways that we believe AI can improve the performance of a BMS.

1. You can increase energy efficiency: Enhancing energy efficiency is one of the most compelling benefits of integrating AI into multi-site facility management. AI's ability to process and analyze vast volumes of real-time data from sensors revolutionizes how energy consumption is optimized. It unlocks the potential to recognize and understand historical energy usage patterns across different facilities with unprecedented ease and accuracy. Moreover, AI's sophistication extends to assimilating external data inputs, such as weather forecasts, storm predictions, and air quality updates. This comprehensive data integration allows AI algorithms to make informed decisions, adjusting HVAC, lighting, and other critical systems in real-time. The result is a significant reduction in energy waste, leading to a decrease in operational costs. By pre-emptively adapting to both internal and external environmental changes, AI contributes to a more sustainable, energy efficient, and cost-effective operation of multi-site facilities.

2. Save money through predictive maintenance: AI can let building managers better predict equipment failures and more accurately anticipate maintenance needs by monitoring the performance of different building components and systems. This leads to adopting a more proactive approach that reduces downtime and maintenance costs.

3. Improve occupant comfort: AI can ingest data from occupancy sensors, weather forecasts, and other sources to create a more comfortable environment for building occupants. It can adjust temperature, lighting, and ventilation to meet user preferences and improve tenant productivity.

4. Safer and more secure buildings: AI-powered security systems can analyze video feeds, access control data, and use sensor information to detect anomalies and potential threats. It can also provide real-time alerts and enhance the overall safety of the building.

5. Better space utilization: AI can analyze data from occupancy sensors, enabling better space utilization and allowing facility managers to make more informed decisions about office layouts and resource allocation.

6. Cost reduction: AI-driven BMS’ can reduce operational costs by automating routine tasks. Examples include scheduling cleaning, lighting control, and equipment usage.

7. Leverage the data: Vast amounts of data are generated by a BMS. AI can leverage it to provide actionable insights. Facility managers can then make data-driven decisions to improve building performance and operational efficiency.

8. Help the environment: AI can support sustainability initiatives by optimizing energy usage, reducing waste, and identifying steps toward greener practices.

9. Remote monitoring and control: AI enables remote monitoring and control of BMS systems. That means facility managers can adjust and respond to issues without needing to be onsite at a building. Better yet, with AI we can enable fully autonomous building operations, where the AI manages buildings based on a group of parameters. The AI model will constantly improve upon itself as it learns how the building reacts to different values, without the facility managers having to perform any actions of their own.

10. Manage big systems and big facilities: Large-scale buildings and extended campuses understandably have complex BMS infrastructures. Additionally, large fleets with hundreds to thousands of buildings cannot be cost-effectively managed by a human 24x7. AI can help to keep an eye on building performances across an entire portfolio, and trigger alerts and take necessary actions when certain conditions exist.   

11. Reduce complexity: AI can be used when we combine data from multiple sources to smooth analytics and reduce silos. Examples are IoT devices and edge computing to create a holistic and interconnected facilities management ecosystem.

Today, large-scale building management is challenging.  Building managers are increasingly handling multiple sites with numerous systems and different technologies. Getting the most out of their company’s investments isn’t easy. But as we’ve seen, AI has the potential to be transformative in how large-scale building management systems perform.

As the technology continues to evolve, AIs impact on BMS is likely to increase, making buildings smarter, more sustainable, and cost-effective to manage.

About NexRev

At NexRev, we’ve been unlocking the power of facility and energy management data with over a million connected devices across North America. Our team of experts is focused on helping you deliver more with your budgets, infrastructure, and assets to create sustainable savings in operations and energy, reducing your risk and increasing operational confidence.

To begin a discussion on how we can help you to reduce energy waste, please email us at This email address is being protected from spambots. You need JavaScript enabled to view it.