Chris Penrose, COO, FogHorn looks at managing energy demand fluctuations of the hybrid work model with real-time analytics.

In a post-pandemic world that’s prompting 79% of executives to adopt hybrid work models allowing employees to only come into the office a few days a week, facilities managers are finding themselves with a strategic challenge: How to adapt their building management systems (BMS) to be more dynamic in adjusting to the fluctuating demand for power that results from these newly flexible schedules.

The fact is, most buildings continue to struggle with BMS systems that are mostly observational and have limited capacity to automatically adjust based on operational conditions, much less in real-time. This is causing organizations to waste significant energy in unoccupied areas and suffer inflated utility costs. It’s also a stumbling block in meeting 2030 greenhouse gas reduction targets for businesses laid out by the White House. Fortunately, the right solutions leveraging AI, edge computing and real-time analytics can improve energy efficiency and decrease operational costs in the face of today’s increasingly unpredictable office schedules.

Heightened demands for agility

Pre-COVID, most large and mixed-use buildings relied on routine work hours to guide BMS systems scheduling for energy-consuming systems like lighting and HVAC – typically anticipating full occupancy Monday through Friday from 7:00 a.m. to 5:00 p.m., which was wasteful in and of itself. Now, as employees return to the office with varying levels of occupancy, facilities managers are tasked with the challenge of accommodating these irregular schedules.

In many ways, the current return to the workplace is even trickier for building managers than during the height of the pandemic – when occupancy in buildings became predictably and uniformly low in the face of public health regulations that left few alternatives to keeping entire workforces at home. Now, more people are returning to the workplace, but in sporadic and unpredictable ways that create agility challenges for facilities personnel and their BMS architectures.

Thanks to pandemic-induced global supply chain disruptions that can lead to fluctuations in inventory levels and how much warehouse space needs to be maintained with power and HVAC to house it, the unpredictability even extends to warehouse energy costs, which can account for nearly 10% of a company’s annual revenue. While these supply chain disruptions are expected to be temporary, the irregular office schedules will likely remain as the “new normal” well beyond the pandemic as part of a permanent trend toward hybrid work models.

Meeting the challenge with Edge AI

Despite these realities, few buildings today are equipped with BMS assets that go beyond the observational or preprogrammed. Manually keeping up with inconsistent occupancy levels can be time-consuming, expensive and wasteful to the environment. For organizations to remain efficient, competitive and sustainable, this must change.

Luckily, BMS agility is becoming easier thanks to a growing range of data-driven options to dynamically adjust building conditions with more real-time visibility and control. These systems afford advanced analytics capabilities that can track and adjust for office schedules, occupancy, weather forecasts, hourly energy rates and HVAC machine health – all of which create operational efficiencies and higher profits for building owners.

Some of the most powerful systems involve AI-enabled edge computing, or edge AI, in which data is processed at or near the source of the data. Especially as IoT devices and sensors proliferate in smart buildings, edge AI avoids the cost and latency that comes from collecting and transmitting data back and forth to offsite cloud services for processing. Additional savings comes from the fact that building operators can install new edge AI onto existing systems without the costly need to rip and replace legacy components.

Predictive maintenance and additional benefits

Smart BMS architectures powered by edge AI can take efficiencies beyond real-time management of energy-consuming systems and into the realm of revenue-saving predictive analytics and preventive maintenance. For example, the operational data from high-frequency vibration sensors can detect irregularities in a building chillers for cooling and apply analytics to predict a future point of failure. This gives the opportunity to proactively fix the problem, avoiding a costly breakdown repair and service interruption that would negatively impact the tenant experience.

Whether the use case is HVAC, lighting or some other building system, edge AI-enabled predictive capabilities extend the maximum effective asset lifetime for expensive equipment. This is especially true given that hybrid work models add wear and tear on systems from more frequent operational adjustments in response to changing occupancy factors. As a bonus, analytics on those shifting occupancy patterns can also help pinpoint the best and least-disruptive times to conduct scheduled maintenance and planned service interruptions.

Finally, with COVID-19 remaining a concern, edge AI can even augment a building’s health security posture with the use of machine vision, infrared sensors and streaming video analytics to verify PPE usage, such as mask wearing, among occupants; or to detect elevated body temperature, which could be a sign of infection. These are just some of the advantages that enhanced BMS capabilities can bring to facilities managers coping with complicated occupancy schedules of an at-risk workforce in the wake of COVID-19.


The “new normal” of hybrid work schedules for a post-pandemic workforce is prompting a much-needed revolution in BMS capabilities. Led by the power of edge AI, these systems are arming building managers with enhanced visibility and real-time agility to react to changing conditions and make automatic adjustments for improved tenant safety, energy efficiency and cost-savings.