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Welcome to our Case Study series, where we dive into case studies of real-life, large-scale deployments of smart building technologies, supported by the Nexus Marketplace.
I emphasize “real life” because this isn’t a marketing fluff story. We're here to share real lessons from leaders who have done the work to integrate smart building technology into their operations. I also emphasize “large scale” because we're not here to talk about pilot projects. We're here to talk about deeper commitments to changing how buildings are operated.
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The Nexus Labs Buyer’s Guide to Metering and Buyer’s Guide to Energy Management both start by summarizing the market trends pushing building owners to find more sophisticated solutions to reducing energy consumption and carbon emissions. First, there are carrots, like volunteer sustainability reporting, publicly released carbon reduction goals, and utility incentives. Then, there are the sticks, like the expanding pool of local performance and disclosure laws and the ever-growing complexity of utility tariff schedules of the over 3,000 utility companies across the United States.
These market influences are no stranger to Macerich, a publicly traded real estate investment trust focusing on malls and having a portfolio of 43 large shopping centers nationwide. Macerich has goals of carbon neutrality by 2030 and net zero carbon emissions by 2035. What is unique to Macerich’s story is the proficiency of their smart buildings program. If you’re a common reader of Nexus Labs, you’ve heard us tell many stories about the importance of laying a foundation of digital infrastructure at the device, network, and data layer before reaping the benefits of innovative application layer software. Macerich, having spent decades on their digital infrastructure, has reached a point where they are starting to see the benefits of advanced applications sitting on top of the technology stack, like automated demand management that can drop HVAC energy consumption by 25%-30% without any impact to comfort.
We sat down with Ryan Knudson of Macerich and Jonathan Schoenfeld of Buildings IoT to learn more about how Macerich is applying new-era technologies to their portfolio in the latest effort to bring them one step closer to their carbon neutrality goals.
A primary obstacle in Macerich’s carbon neutrality targets is reducing the effect of scope two emissions. While scope one emissions represent the greenhouse gas (GHG) emissions coming directly from Macerich’s buildings (e.g., a natural gas burning HVAC unit), scope two emissions represent the GHG emissions coming from the purchase of electricity and other utilities that create GHG emissions off Macerich’s property. For example, Macerich purchases electricity from a utility provider that generates electricity by burning natural gas.
To Macerich, avoiding scope two emissions starts with finding technologies to help buildings adapt to new grid transitions. “The utilities are transferring from fossil fuels to renewables; the buildings need to be able to adapt to new supply conditions,” explained Knudson.
Ryan Knudson is VP of Corporate Responsibility and Sustainability at Macerich. He’s been with Macerich for over 13 years. He started in the IT department as a building automation technical expert and has since transitioned to leadership on the sustainability team. Knudson joined Nexus Labs on a podcast in March of 2022 titled “The ESG Data Gap,” where he discussed the challenges of getting the correct data from your sites for sustainability reporting. That conversation foreshadowed the applications Macerich is applying today…
In The ESG Data Gap, Knudson discusses the program Macerich called EMS (Energy Management System) 2.0, colloquially referred to by Knudson as “unlocking the building.” In 2022, Macerich focused on using Tridium Niagara integrations to build BACnet infrastructure and uniform sequences of operations across all of their building automation equipment. This was, in essence, the development of the correct digital infrastructure at the device and network layer to stack advanced technologies on top.
Macerich refers to their current program in 2024 as EMS 2.5. It is a continuation of the same building management program they have been developing for the last 15 years. EMS 2.5 is an effort to stack Fault Detection and Diagnostics (FDD) and Automated Demand Management (ADM) on top of the infrastructure Macerich built.
“We were looking at all the investments we’ve made in our controls systems and were trying to find new ways to leverage the compute power to derive new value. We wanted to find a frictionless way of automating our demand management and controls without the need for human intervention.” —Knudson
From EMS 2.0 to EMS 2.5, Macerich has partnered with Buildings IoT as a trusted partner in their smart building journey. John Schoenfeld, the VP of Energy and Building Technology and Buildings IoT, has supported this Macerich project over the last eight years. Schoenfeld recollected the growth and transformation he’s seen in Buildings IoT during his eight years there, almost as if the maturity of the Buildings IoT offering grew parallel to the Macerich smart building program. Schoenfeld discussed how Buildings IoT started as a systems integrator. While integrating disparate systems together, Buildings IoT developed their version of an Independent Data Layer (IDL) called IoT Jetstream and is now supporting customers like Macerich in FDD and ADM.
Macerich began piloting Buildings IoT software for FDD and ADM in July 2022. As of May 2024, Macerich has rolled the solution out to 8 properties and sees an annual utility cost savings of approximately $100,000 per site.
Despite the solid technology stack Macerich was building upon, adopting FDD and ADM applications requires careful integration and change management. We had Knudson and Schoenfeld dive into the technical considerations that have made EMS 2.5 successful thus far.
Knudson explained how Macerich looked at a handful of applications that reported ADM capabilities. The first important factor to Macerich was the cost of implementation. They questioned vendors about their solution’s ability to bolt onto Macerich’s existing infrastructure. For Macerich, the implementation cost wasn’t only capital expenses, as Knudson explained, “While the initial cost from B2B may be low, my internal resources have to manage this. We really took into consideration the total cost of ownership.”
Beyond the implementation and ownership costs, it was important for Macerich to understand the energy savings capabilities and the strategy for change management. Knudson continued to emphasize the concept of a frictionless addition: for automated demand management to be effective, it needs to be seen as a force multiplier to his facilities teams and for guests of the building, not a burden.
After deciding to proceed with Buildings IoT, Macerich chose a Connecticut shopping center as an ideal pilot location. The Connecticut location had diverse systems eligible for ADM control, including direct expansion (DX) air conditioning, a central plant, and lighting control. Enrolling more diverse systems in ADM would give Macerich more data on what this program was capable of beyond the pilot.
“We did a pilot 2022, but we didn’t really start rolling out in earnest until Q3 of 2023 because we really wanted to take our time and not have a situation where everyone gets excited that we deployed something new and four months later it’s turned off.” —Knudson
Knudson credits some of the early success of the ADM rollout to Macerich’s patience in the program. While it can be tempting to look at savings over a short period, it was important for Macerich to understand not only the seasonal changes to savings but also the operational adjustments that the local facilities teams had to endure to prove that this project was sustainable for an extended period.
After the successful six-month pilot, Knudson and his team were confident enough to begin rolling out the application to the following seven properties. By the end of 2025, Macerich plans to have ADM capabilities in about 26 malls nationwide.
Deploying an application on top of existing infrastructure can seem almost too simplistic. Schoenfeld and Knudson walked us through what it actually takes to implement ADM.
“In order to do all this, you need to have connectivity. Coordinating with the IT team is key, because you’ll need to install an edge-to-cloud gateway at every single site” – Schoenfeld
ADM applications rely on cloud computing, and therefore, a gateway that can efficiently communicate between the on-prem building systems and the cloud is essential. Schoenfeld reminded us of the importance of early and frequent collaboration with the IT team, who need to understand the project context to help support a safe portal for information to come and go from the property to the cloud.
With building systems communicating to and from the cloud through installed edge devices, Schoenfeld reinforced the importance of starting with FDD before ADM. Simply put, you can only optimize a building using Automated Demand Management after first completing Fault Detection and Diagnostics.
Advanced Supervisory Control (ASC) methods, like Automated Demand Management, use adjustments of setpoints, speeds, and schedules of equipment like levers to control the amount of energy a building consumes. If the system believes it can positively manipulate building operation by adjusting one of these levers, only to find the lever has malfunctioned, the whole process falls apart. For example, imagine an HVAC air handling unit (AHU) with a variable frequency drive (VFD) in a fault state that cannot reduce speed below 100%. If the optimization sequences attempts to save energy by commanding a speed reduction to the VFD, the system will not respond, and no energy will be saved.
Buildings IoT provided an FDD solution that gave Macerich facility managers a prioritized and actionable list of issues to fix before enrolling equipment in the ADM program. If a fault has been identified in equipment, it must be addressed before it can be enrolled in ADM.
For further information on everything needed to unlock automated demand management, we recommend you check out our previous article on The 8 Enablers to Grid-Interactive Efficient Buildings.
Assuming a successful edge gateway connection and resolved equipment issues, Schoenfeld expressed how the Automated Demand Management application models data and takes action primarily based on pre-cooling, load-shed algorithms, and understanding utility tariffs.
Generally, data modeling involves ingesting massive amounts of data and organizing, ensuring consistency, and defining how different data elements interact with one another. In the Buildings IoT case, this means understanding how schedules, setpoints, occupancy rates, heat loads, equipment capacities, and weather data interact with one another and how variances in any one piece of data affect the whole system.
One classic way ADM optimizes building energy consumption is through pre-cooling: understanding an upcoming cooling demand and addressing it early to benefit from things like cleaner energy, cheaper energy, or a more conservative and gradual ramp-up. A cloud-based system can model setpoints, occupancy schedules, energy sources available, and forecasted weather data to create a cooling sequence that may be too complex for a single human to develop and implement.
In addition to pre-cooling, the concept of load-shed is also present. Schoenfeld described the Buildings IoT load-shed algorithm approach as follows: "We are making small adjustments to zone setpoints in a sophisticated manner to try to minimize impact to occupants but maximize the load-shed on a 5-minute basis.”
Through strategies like pre-cooling and load-shed, Buildings IoT can effectively react to changes in the grid, lower carbon consumption, and save Macerich operating expenses.
Schoenfeld explained the importance of decoupling the pre-cooling and load-shedding operations from the process variable to which the load-shed is reacting. Each Macerich property has different influences and metrics to which one may want to control an ADM program. For example, some properties may have extreme peak demand charges to avoid, others may have hours of dirty power to avoid, and some may not even have the appropriate meters to measure consumption. By decoupling the sequence of load-shed from the process variable, Buildings IoT can create the best optimization solution regardless of what is most important or what is readily available information to any particular property.
Creating load-shedding algorithms independent of the process variable means that the results seen at each store are as unique as the grid, weather, equipment, and utility rates at each property. Knudson shared the results of three particular properties.
#1 - Regional Center in New York City
In the FDD stage before ADM, the Buildings IoT platform was capable of recognizing 15 RTUs that, although scheduled to be off at night, had an integration problem between local controls and the Niagara system that was causing the units to run 24/7, unbeknownst to the building operators. Fixing the schedule of these units caused a 20% reduction in HVAC consumption before any ADM techniques were implemented. After the adjustment to the RTU schedule, load-shed was implemented from 4pm until close, resulting in approximately 150kW of power savings with no impact to the occupants.
#2 - Regional Mall in Southern California
ADM at this mall resulted in a 1% energy reduction. At the same time, the utility implemented a 15% increase in rate structure for Macerich. ADM saved Macerich 15% on their utility bill without any changes to the operators' behavior.
#3 - Multi-use Center in Northern Virginia
Perhaps the best example of true ADM, this property saw 250 kW (25-30% of total HVAC load) being load-shed daily without impacting comfort or causing rebound spikes afterward. This was mainly the result of minimizing consumption during 3 and 4 p.m. when power is most expensive and the grid is dirtiest.
These case study articles aim to share success stories in a way that can benefit similar building owners by helping them make accelerated decisions toward improved tech. Knudson was willing to share the largest obstacles to getting to where they are today, hoping other building owners could benefit from the shared knowledge.
Knudson restated the importance of good metering as a reminder of the pre-work that went into Macerich’s device layer to get to this point. Macerich is currently monitoring utility meters at the master level, but at locations where they have more granular metering capabilities, the ADM program can do a lot more sophisticated control. In regions where Macerich has less sophisticated meter data, the ADM program typically relies on an occupancy schedule only for load-shed and cannot avoid peak demand charges as easily.
Change management may be the most critical aspect of a successful ADM deployment. ADM attempts to measure more variables than a human can and automatically makes adjustments that a human may not fully understand. This means taking some control out of the hands of facility operators who have decades of expertise in their field.
Knudson explained Macerich’s approach to this as a crawl, walk, run. Part of the benefit of Macerich’s slow pilot phase was the ability to provide context to the facilities teams. “That transparency is key. Operators need context to why, what, and when,” explained Knudson.
The building automation graphics on Macerich properties clearly indicate which pieces of equipment are and are not enrolled in the ADM program. This avoids operators wondering why equipment may be doing something they didn’t tell them to.
This transparency becomes especially important when equipment is operating in ways that wouldn’t be expected. We discussed the concept of pre-cooling above, but Knudson brought up examples of ADM actually performing the opposite of pre-cooling in some instances. Where Macerich has bountiful PV production capabilities, and the utility grid is dirty, Macerich properties will often wait until the sun has fully risen to provide any cooling to the store. “I would much rather have my HVAC running at full tilt when my solar is running at full tilt than having my HVAC running at 50% when my solar is at zero,” noted Knudson. This is an excellent example of the type of control sequence an ADM application may apply, which wouldn’t be so apparent to a building operator.
The results of ADM will vary widely across a portfolio of buildings. A straightforward example of this is the disparity of rate structures from utilities in different areas. Knudson discussed how time of use (TOU) pricing is extremely different across his portfolio: one property may have the highest pricing at 4 to 6 p.m. while others may be in the middle of the day. One of the challenges of deploying a universal ADM program was the continual tweaking and tuning of the base concept to achieve the best results at each property. “Property A will not have the same exact value prop as property B,” Knudson explained.
Macerich’s Automated Demand Management program is in its infancy. Still, it is an excellent example of a modern-age application that building owners can stack on top of their digital infrastructure if set up appropriately. As the industry charges forward with an aging fossil fuel-based grid and a swath of clean energy technologies being implemented on a more micro scale, the complexity of grid interaction within buildings will only grow. Relying on cloud-based applications' processing power and data modeling capabilities may be one of the industry’s best solutions for decarbonizing the built environment.
Welcome to our Case Study series, where we dive into case studies of real-life, large-scale deployments of smart building technologies, supported by the Nexus Marketplace.
I emphasize “real life” because this isn’t a marketing fluff story. We're here to share real lessons from leaders who have done the work to integrate smart building technology into their operations. I also emphasize “large scale” because we're not here to talk about pilot projects. We're here to talk about deeper commitments to changing how buildings are operated.
---
---
The Nexus Labs Buyer’s Guide to Metering and Buyer’s Guide to Energy Management both start by summarizing the market trends pushing building owners to find more sophisticated solutions to reducing energy consumption and carbon emissions. First, there are carrots, like volunteer sustainability reporting, publicly released carbon reduction goals, and utility incentives. Then, there are the sticks, like the expanding pool of local performance and disclosure laws and the ever-growing complexity of utility tariff schedules of the over 3,000 utility companies across the United States.
These market influences are no stranger to Macerich, a publicly traded real estate investment trust focusing on malls and having a portfolio of 43 large shopping centers nationwide. Macerich has goals of carbon neutrality by 2030 and net zero carbon emissions by 2035. What is unique to Macerich’s story is the proficiency of their smart buildings program. If you’re a common reader of Nexus Labs, you’ve heard us tell many stories about the importance of laying a foundation of digital infrastructure at the device, network, and data layer before reaping the benefits of innovative application layer software. Macerich, having spent decades on their digital infrastructure, has reached a point where they are starting to see the benefits of advanced applications sitting on top of the technology stack, like automated demand management that can drop HVAC energy consumption by 25%-30% without any impact to comfort.
We sat down with Ryan Knudson of Macerich and Jonathan Schoenfeld of Buildings IoT to learn more about how Macerich is applying new-era technologies to their portfolio in the latest effort to bring them one step closer to their carbon neutrality goals.
A primary obstacle in Macerich’s carbon neutrality targets is reducing the effect of scope two emissions. While scope one emissions represent the greenhouse gas (GHG) emissions coming directly from Macerich’s buildings (e.g., a natural gas burning HVAC unit), scope two emissions represent the GHG emissions coming from the purchase of electricity and other utilities that create GHG emissions off Macerich’s property. For example, Macerich purchases electricity from a utility provider that generates electricity by burning natural gas.
To Macerich, avoiding scope two emissions starts with finding technologies to help buildings adapt to new grid transitions. “The utilities are transferring from fossil fuels to renewables; the buildings need to be able to adapt to new supply conditions,” explained Knudson.
Ryan Knudson is VP of Corporate Responsibility and Sustainability at Macerich. He’s been with Macerich for over 13 years. He started in the IT department as a building automation technical expert and has since transitioned to leadership on the sustainability team. Knudson joined Nexus Labs on a podcast in March of 2022 titled “The ESG Data Gap,” where he discussed the challenges of getting the correct data from your sites for sustainability reporting. That conversation foreshadowed the applications Macerich is applying today…
In The ESG Data Gap, Knudson discusses the program Macerich called EMS (Energy Management System) 2.0, colloquially referred to by Knudson as “unlocking the building.” In 2022, Macerich focused on using Tridium Niagara integrations to build BACnet infrastructure and uniform sequences of operations across all of their building automation equipment. This was, in essence, the development of the correct digital infrastructure at the device and network layer to stack advanced technologies on top.
Macerich refers to their current program in 2024 as EMS 2.5. It is a continuation of the same building management program they have been developing for the last 15 years. EMS 2.5 is an effort to stack Fault Detection and Diagnostics (FDD) and Automated Demand Management (ADM) on top of the infrastructure Macerich built.
“We were looking at all the investments we’ve made in our controls systems and were trying to find new ways to leverage the compute power to derive new value. We wanted to find a frictionless way of automating our demand management and controls without the need for human intervention.” —Knudson
From EMS 2.0 to EMS 2.5, Macerich has partnered with Buildings IoT as a trusted partner in their smart building journey. John Schoenfeld, the VP of Energy and Building Technology and Buildings IoT, has supported this Macerich project over the last eight years. Schoenfeld recollected the growth and transformation he’s seen in Buildings IoT during his eight years there, almost as if the maturity of the Buildings IoT offering grew parallel to the Macerich smart building program. Schoenfeld discussed how Buildings IoT started as a systems integrator. While integrating disparate systems together, Buildings IoT developed their version of an Independent Data Layer (IDL) called IoT Jetstream and is now supporting customers like Macerich in FDD and ADM.
Macerich began piloting Buildings IoT software for FDD and ADM in July 2022. As of May 2024, Macerich has rolled the solution out to 8 properties and sees an annual utility cost savings of approximately $100,000 per site.
Despite the solid technology stack Macerich was building upon, adopting FDD and ADM applications requires careful integration and change management. We had Knudson and Schoenfeld dive into the technical considerations that have made EMS 2.5 successful thus far.
Knudson explained how Macerich looked at a handful of applications that reported ADM capabilities. The first important factor to Macerich was the cost of implementation. They questioned vendors about their solution’s ability to bolt onto Macerich’s existing infrastructure. For Macerich, the implementation cost wasn’t only capital expenses, as Knudson explained, “While the initial cost from B2B may be low, my internal resources have to manage this. We really took into consideration the total cost of ownership.”
Beyond the implementation and ownership costs, it was important for Macerich to understand the energy savings capabilities and the strategy for change management. Knudson continued to emphasize the concept of a frictionless addition: for automated demand management to be effective, it needs to be seen as a force multiplier to his facilities teams and for guests of the building, not a burden.
After deciding to proceed with Buildings IoT, Macerich chose a Connecticut shopping center as an ideal pilot location. The Connecticut location had diverse systems eligible for ADM control, including direct expansion (DX) air conditioning, a central plant, and lighting control. Enrolling more diverse systems in ADM would give Macerich more data on what this program was capable of beyond the pilot.
“We did a pilot 2022, but we didn’t really start rolling out in earnest until Q3 of 2023 because we really wanted to take our time and not have a situation where everyone gets excited that we deployed something new and four months later it’s turned off.” —Knudson
Knudson credits some of the early success of the ADM rollout to Macerich’s patience in the program. While it can be tempting to look at savings over a short period, it was important for Macerich to understand not only the seasonal changes to savings but also the operational adjustments that the local facilities teams had to endure to prove that this project was sustainable for an extended period.
After the successful six-month pilot, Knudson and his team were confident enough to begin rolling out the application to the following seven properties. By the end of 2025, Macerich plans to have ADM capabilities in about 26 malls nationwide.
Deploying an application on top of existing infrastructure can seem almost too simplistic. Schoenfeld and Knudson walked us through what it actually takes to implement ADM.
“In order to do all this, you need to have connectivity. Coordinating with the IT team is key, because you’ll need to install an edge-to-cloud gateway at every single site” – Schoenfeld
ADM applications rely on cloud computing, and therefore, a gateway that can efficiently communicate between the on-prem building systems and the cloud is essential. Schoenfeld reminded us of the importance of early and frequent collaboration with the IT team, who need to understand the project context to help support a safe portal for information to come and go from the property to the cloud.
With building systems communicating to and from the cloud through installed edge devices, Schoenfeld reinforced the importance of starting with FDD before ADM. Simply put, you can only optimize a building using Automated Demand Management after first completing Fault Detection and Diagnostics.
Advanced Supervisory Control (ASC) methods, like Automated Demand Management, use adjustments of setpoints, speeds, and schedules of equipment like levers to control the amount of energy a building consumes. If the system believes it can positively manipulate building operation by adjusting one of these levers, only to find the lever has malfunctioned, the whole process falls apart. For example, imagine an HVAC air handling unit (AHU) with a variable frequency drive (VFD) in a fault state that cannot reduce speed below 100%. If the optimization sequences attempts to save energy by commanding a speed reduction to the VFD, the system will not respond, and no energy will be saved.
Buildings IoT provided an FDD solution that gave Macerich facility managers a prioritized and actionable list of issues to fix before enrolling equipment in the ADM program. If a fault has been identified in equipment, it must be addressed before it can be enrolled in ADM.
For further information on everything needed to unlock automated demand management, we recommend you check out our previous article on The 8 Enablers to Grid-Interactive Efficient Buildings.
Assuming a successful edge gateway connection and resolved equipment issues, Schoenfeld expressed how the Automated Demand Management application models data and takes action primarily based on pre-cooling, load-shed algorithms, and understanding utility tariffs.
Generally, data modeling involves ingesting massive amounts of data and organizing, ensuring consistency, and defining how different data elements interact with one another. In the Buildings IoT case, this means understanding how schedules, setpoints, occupancy rates, heat loads, equipment capacities, and weather data interact with one another and how variances in any one piece of data affect the whole system.
One classic way ADM optimizes building energy consumption is through pre-cooling: understanding an upcoming cooling demand and addressing it early to benefit from things like cleaner energy, cheaper energy, or a more conservative and gradual ramp-up. A cloud-based system can model setpoints, occupancy schedules, energy sources available, and forecasted weather data to create a cooling sequence that may be too complex for a single human to develop and implement.
In addition to pre-cooling, the concept of load-shed is also present. Schoenfeld described the Buildings IoT load-shed algorithm approach as follows: "We are making small adjustments to zone setpoints in a sophisticated manner to try to minimize impact to occupants but maximize the load-shed on a 5-minute basis.”
Through strategies like pre-cooling and load-shed, Buildings IoT can effectively react to changes in the grid, lower carbon consumption, and save Macerich operating expenses.
Schoenfeld explained the importance of decoupling the pre-cooling and load-shedding operations from the process variable to which the load-shed is reacting. Each Macerich property has different influences and metrics to which one may want to control an ADM program. For example, some properties may have extreme peak demand charges to avoid, others may have hours of dirty power to avoid, and some may not even have the appropriate meters to measure consumption. By decoupling the sequence of load-shed from the process variable, Buildings IoT can create the best optimization solution regardless of what is most important or what is readily available information to any particular property.
Creating load-shedding algorithms independent of the process variable means that the results seen at each store are as unique as the grid, weather, equipment, and utility rates at each property. Knudson shared the results of three particular properties.
#1 - Regional Center in New York City
In the FDD stage before ADM, the Buildings IoT platform was capable of recognizing 15 RTUs that, although scheduled to be off at night, had an integration problem between local controls and the Niagara system that was causing the units to run 24/7, unbeknownst to the building operators. Fixing the schedule of these units caused a 20% reduction in HVAC consumption before any ADM techniques were implemented. After the adjustment to the RTU schedule, load-shed was implemented from 4pm until close, resulting in approximately 150kW of power savings with no impact to the occupants.
#2 - Regional Mall in Southern California
ADM at this mall resulted in a 1% energy reduction. At the same time, the utility implemented a 15% increase in rate structure for Macerich. ADM saved Macerich 15% on their utility bill without any changes to the operators' behavior.
#3 - Multi-use Center in Northern Virginia
Perhaps the best example of true ADM, this property saw 250 kW (25-30% of total HVAC load) being load-shed daily without impacting comfort or causing rebound spikes afterward. This was mainly the result of minimizing consumption during 3 and 4 p.m. when power is most expensive and the grid is dirtiest.
These case study articles aim to share success stories in a way that can benefit similar building owners by helping them make accelerated decisions toward improved tech. Knudson was willing to share the largest obstacles to getting to where they are today, hoping other building owners could benefit from the shared knowledge.
Knudson restated the importance of good metering as a reminder of the pre-work that went into Macerich’s device layer to get to this point. Macerich is currently monitoring utility meters at the master level, but at locations where they have more granular metering capabilities, the ADM program can do a lot more sophisticated control. In regions where Macerich has less sophisticated meter data, the ADM program typically relies on an occupancy schedule only for load-shed and cannot avoid peak demand charges as easily.
Change management may be the most critical aspect of a successful ADM deployment. ADM attempts to measure more variables than a human can and automatically makes adjustments that a human may not fully understand. This means taking some control out of the hands of facility operators who have decades of expertise in their field.
Knudson explained Macerich’s approach to this as a crawl, walk, run. Part of the benefit of Macerich’s slow pilot phase was the ability to provide context to the facilities teams. “That transparency is key. Operators need context to why, what, and when,” explained Knudson.
The building automation graphics on Macerich properties clearly indicate which pieces of equipment are and are not enrolled in the ADM program. This avoids operators wondering why equipment may be doing something they didn’t tell them to.
This transparency becomes especially important when equipment is operating in ways that wouldn’t be expected. We discussed the concept of pre-cooling above, but Knudson brought up examples of ADM actually performing the opposite of pre-cooling in some instances. Where Macerich has bountiful PV production capabilities, and the utility grid is dirty, Macerich properties will often wait until the sun has fully risen to provide any cooling to the store. “I would much rather have my HVAC running at full tilt when my solar is running at full tilt than having my HVAC running at 50% when my solar is at zero,” noted Knudson. This is an excellent example of the type of control sequence an ADM application may apply, which wouldn’t be so apparent to a building operator.
The results of ADM will vary widely across a portfolio of buildings. A straightforward example of this is the disparity of rate structures from utilities in different areas. Knudson discussed how time of use (TOU) pricing is extremely different across his portfolio: one property may have the highest pricing at 4 to 6 p.m. while others may be in the middle of the day. One of the challenges of deploying a universal ADM program was the continual tweaking and tuning of the base concept to achieve the best results at each property. “Property A will not have the same exact value prop as property B,” Knudson explained.
Macerich’s Automated Demand Management program is in its infancy. Still, it is an excellent example of a modern-age application that building owners can stack on top of their digital infrastructure if set up appropriately. As the industry charges forward with an aging fossil fuel-based grid and a swath of clean energy technologies being implemented on a more micro scale, the complexity of grid interaction within buildings will only grow. Relying on cloud-based applications' processing power and data modeling capabilities may be one of the industry’s best solutions for decarbonizing the built environment.
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