The Compounding Value of Technology, Part IV: Energy Savings

Energy savings has become a polarizing topic in commercial real estate.

Some believe that direct energy savings to landlord-controlled utilities is the only way technology can deliver value when it comes to building operations (one of the reasons we’ve created this series).

Others have been burned on real time energy monitoring pilot projects and have effectively given up on the prospect as a nice idea, but not grounded in reality.

A small third category have stayed above the fray and, due to recent rising operating expenses, are looking into it for the first time. Many in this bucket struggle to differentiate between solutions that sound ostensibly the same. How, for example, are they supposed to compare the effectiveness of different vendors’ analytical capabilities?

Even when operational changes are made, or equipment is retrofitted or upgraded, is it possible to see energy savings? Utility bills vary wildly based on the weather and occupancy patterns. Do you need to wait a whole year to determine if there was an effect?

In the scope of areas of value that technology can deliver, energy savings is one of the most straightforward. Unlike productivity gains, every dollar flows directly to the bottom line.

All that’s needed is a comprehensive understanding of the different approaches, and the types, and magnitudes of insights that can be derived from each.

Master Meter Analytics

Master meter analytics involve collecting building-level (or sometimes just common area) data to gain real-time transparency into how much energy is being consumed.

This approach offers the benefit of being relatively low cost to deploy. However, due to the lack of granularity of the data, the insights are also relatively limited.

The most common insight that can be derived from this data is around peak demand shaving.

For example, with building-level data, analytics can clearly identify that there is a large spike in demand right as the building ramps up in the morning (a common scenario in office assets).

This is a problem because utilities determine their rate based on the highest amount of demand in a 15 minute interval during a billing period. So, if demand spikes even once in a month, it is going to increase the cost of electricity for the whole month.

The master data wouldn’t tell you which equipment is being turned on, but a reasonably proficient engineer would be able to take this data and make some adjustments to smooth out the ramp up, thus lowering peak demand and associated costs.

One thing to note is that this is not actually reducing the carbon emissions of the building, just the utility costs.

Tenant Meter Analytics

The majority of conversations around energy consumption focus on base building energy savings that accrue to the landlord and provide a compelling ROI.

However, as investor and regulatory pressure focuses on whole-building carbon footprints as a measure of success, landlords are increasingly wondering what they can do to help tenants reduce their energy consumption.

Whether tenants are submetered for billing purposes or shadow metered to satisfy ESG reporting requirements, landlords can provide analytics as a value add service.

These insights are most similar to master meter data with the exception of being able to also identify schedule violations and high idling loads because the data is focused on a specific space, rather than an entire building.

There is no guarantee that tenants will make the changes, but the ability to support their goals is becoming a factor in attracting the highest quality tenants.

Building Management System (BMS) Analytics

Not all properties have a building management system for automating equipment controls. Even in those that do, some are so old that data cannot be extracted.

However, in the segment of properties for which BMS exist and are on an open protocol (such as BACnet), providers can provide an analytical layer to optimize BMS configurations.

The best way to think about it is the difference between a keyboard and Grammarly. The keyboard will execute whatever you tell it precisely. Grammarly will tell you if the sentence you wrote makes any sense.

The big difference is that buildings and complex environments, not only technically, but because there are so many humans involved, each with competing priorities and incentives.

BMS configurations are often optimal when the building is first commissioned. However, through countless small adjustments and changes, it inevitably becomes out of whack. The building works, but not optimally (it wastes energy).

An analytical overlay would determine how to realign the configurations to their optimal place. This is equipment-level data, so the optimizations would be very precise, but most BMS do not capture energy data, so determining the savings requires additional calculations.

There are some insights that are shared with equipment monitoring (see next section), and some that can only be derived with BMS data.

Common insights are around HVAC schedules and temperature set points. BMS-specific insights include simultaneous heating and cooling and air-side economization.

Equipment Monitoring

Equipment monitoring involves deploying equipment-level submetering, either as a complement to BMS data or for buildings without a BMS.

This approach combines the best of both worlds - granularity and energy data - to generally deliver more valuable insights.

As mentioned, some are common to the types of insights that BMS data can deliver, such as HVAC schedule optimization and temperature set points.

There are others that are only available through equipment monitoring. For example, unlike master meter data, equipment monitoring can pinpoint exactly which equipment are driving peak demand charges.

Likewise, because it’s energy data, equipment monitoring can be used to determine accurate overtime HVAC billing rates, something that cannot be done with BMS data alone.

In addition to the operational insights, equipment monitoring tracks the carbon emissions associated with individual pieces of equipment, helping to inform capital planning decisions.

Furthermore, the measurement and verification of savings do not require any calculations or assumptions, they can be directly measured as soon as the conservation measure is implemented.

Conclusion

There are several tools and strategies to leverage technology to optimize energy usage and reduce carbon emissions.

Often, the right strategy comes down to the portfolio makeup. Whether the leases are gross or triple net, the size of the properties, whether there is a BACnet-compatible BMS in place, etc.

By understanding the difference between these strategies, asset managers can make more informed decisions on what to deploy, and when.

Likewise, by building on the operator buy-in, tech consolidation, and tenant utility recovery mentioned in previous articles, these insights are much more likely to be acted upon swiftly and with confidence.