The Building Management System —aka the BMS— is the most underrated software toolset utilized today. The BMS is an essential part of any >10,000 m2 (107,639 ft2) building. Managing and configuring it correctly requires deep technical skills and an understanding of basic control PID loops, mechanical principals and thermal dynamics. This capital-intensive, mechanical equipment component —the complete BMS and software toolset— is deeply under-appreciated.
In an age before TCP/IP, Ethernet, and three-dimensional graphics, the BMS had to physically connect to multiple end points — usually on twisted-pair copper wire. The BMS was tasked with coordinating multiple events in a uniform fashion and on a pre-set schedule, irrespective of external factors like outside weather conditions. Building engineers and facilities managers were required to understand the complex mechanics of a million-dollar chiller or boiler that was pumping cool or hot air through an intricate web of air handling units, ducts, fans, dampers, and other mechanical devices. Simultaneously, facilities managers were responsible for keeping occupants comfortable and immune to the often hostile outside conditions (think -20°F in Buffalo, New York). Since most work is done behind the scenes, the facilities management profession often goes unnoticed and underappreciated.
Today, things are changing. IoT, big data, and AI are challenging the role of the BMS and its place in the built environment. Most BMSs have been humming along within their buildings for many years. They’ve been operating at a level of reliability and steadiness that most app developers, with their abundant toolchains, would envy.
The BMS and its associated controllers operate as a harmonious, real-time engine continually updating and monitoring damper positions, valves’ hardware, zone temperatures, and many other intricate variables and data points. This function of real-time response, based on real-time conditions with autonomous, high-level control, will continue to exist and prosper. In fact, it will be augmented with and enhanced by AI — but not replaced.
The growth of data sets and the digitization of our environments are providing greater opportunities to define and develop efficient strategies for how we can condition our collaborative spaces. Machine learning, and more broadly AI, are providing better algorithms that can take in larger data sets and work out unforeseen relationships between data points — such as time-of-day, humidity, occupancy, air quality, demand events, and a host of other variables not yet considered. It is the output of these algorithms that can better instruct the BMS, and its controllers, to achieve more optimal and efficient strategies. The goals of these strategies can vary from cost-based, to demand- or even comfort-based, founded on patterns and algorithms that can be guided and set by in-house teams or remote operators.
This type of data and algorithmic thinking requires tools that don’t normally exist in the BMS, but are found in abundance in the “cloud.” There is a symbiotic relationship between the highly reliable, physical BMS and the seemingly limitless power of the cloud and AI. This relationship between ever larger data sets and real-time responsiveness must be honoured and valued. The cloud and its analytic and machine learning capabilities provide a powerful avenue to expand and improve upon the existing BMS and its real-time responsibilities.
Imagine that your new, fancy V8 Dodge Charger engine is programmed with more advanced and economical responsive driving strategies compared to previous models, which, in turn, could measurably prolong its life and reduce service visits. Furthermore, it doesn’t matter if it is a mother, father or their 18-year-old who is taking out the prized possession for a spin. Having an advanced driving strategy available, and clearly visible for activation, to enhance your vehicle’s life would be reassuring — especially if your kid has only been driving for a year. Of course, it is always possible to switch to a local override. However, the ability to drive the car more efficiently and economically — to get more miles and longer life out of the mechanical masterpiece — is there.
Building managers know their buildings and the control strategies that are best for their buildings. Augmenting existing BMSs and building practises with cloud-based toolsets, allows building managers to scale and focus on other responsibilities. A BMS will always require a facilities/building manager responding in real-time to current conditions and events. It is the tools to support and monitor those events and conditions that are rapidly changing. BuildingIQ has spent years investing in research and development, studying the fine line between AI-based control and real-time responsiveness. As the demands for ever more complex temperature settings, access requirements, lighting, and safety needs increase, AI and cloud technology can help streamline those new demands providing more insights, recommendations, and automated actions for the BMS and building management teams.
AI and cloud-based toolsets do not replace Building Management Systems for large buildings, they add a complementary layer.
Adam Benson is VP of Engineering and DevOps at BuildingIQ. Adam provides the guidance and leadership to bring to market a platform of services that enable today’s buildings to be smarter and greener.