Nexus #13 (3/10/2020)

75F's uniqueness; designing system-level KPIs; Tesla's lessons for disruption

👋 Welcome to Nexus, a newsletter for smart people applying smart building technology—written by James Dice.

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And by the way: if you missed last week’s edition, you can find it here.


My latest ideas

+ Why building analytics are inevitable—my response to a recent discussion questioning whether analytics will go mainstream.

+ The latest LinkedIn discussion:

Is this BS?

Recently I’ve seen several analytics firms claiming to be able to set up their hardware and software in less than half a day. Some say it takes under an hour.

"By automating these tasks through advanced algorithms, the time to onramp a new building has been reduced by more than 90 percent. Recently, we have successfully onboarded multiple commercial buildings in under an hour (fully mapped and tagged with analytics online).”

Add your thoughts on LinkedIn


Ideas from elsewhere

+ What makes 75F unique? (Building Automation Monthly podcast)—We talked about Bill Gates’s investment in 75F way back in Nexus #1. This podcast conversation between Phil Zito and 75F’s CEO unpacks what makes this up-and-coming building automation system (BAS) vendor unique. Let’s walk through the top differentiators:

  • They’re a full-stack solution—sensors, controllers, edge software, cloud software. And the cloud software can be containerized and put into anyone’s data center environment for security. Their sensors combine a bunch of measurements together to avoid installation costs of multiple sensors:

  • Designing for a full stack allows the system to be more plug-and-play—meaning it’s easier to install than your average BAS. They say it’s so easy a 9-year-old could do it.

  • They push automatic sequence updates that match the latest version of ASHRAE Guideline 36P - High Performance Sequences of Operation for HVAC Systems. They feature dynamic air balancing control, chilled water flow optimization, outdoor air optimization, and they even push predictive updates to sequences based on machine learning computations in the cloud.

  • Their suitability for small commercial buildings. According to U.S. Energy Information Administration statistics from the Commercial Buildings Energy Consumption Survey, 95% of commercial buildings in the U.S. are 50,000 square feet in size or smaller. These buildings are historically under-automated and under-optimized.

    A specific use case seems to be small constant volume systems with bypass dampers:

    Until now, unitary RTUs with Constant Volume needing zone control would have been an application for VVT with bypass damper. The traditional VVT zone controls utilize bypass dampers to relieve back pressure that may damage the package unit. 75F's continuous commissioning system reduces the need for bypass dampers, saving energy and equipment costs.


+ Developing System-Level KPIs—A few weeks ago, in Nexus #10, we discussed system-level KPIs as an underutilized analytics strategy. I went deeper on this topic by checking out an ASHRAE Transactions paper written by LBNL last year¹.

As more and more buildings are outfitted with system-level submetering (thanks to ASHRAE Standard 90.1 starting in 2013), the next step is to actually do something with all that meter data. Here’s a strategy any analytics practitioner can follow based on the paper:

First, develop a suite of KPIs that cover all the systems (HVAC, Lighting, Plug Loads, etc) in the building. You design them using four strategies:

  • Normalization - calculating energy use and demand use for each system per square foot/occupant/degree day/etc.

  • Controllability - leveraging the installed control strategies to look for issues. E.g. an average summer weekday’s lighting energy consumption should be less than winter if daylighting controls are working properly. 

  • Correlation - determining how well is each system is responding to the service demand. E.g. domestic hot water use should be correlated with occupancy. Cooling energy should be correlated with outdoor air temperature and wet bulb. Ventilation rate should be correlated with occupant count. 

  • Tailor to the actual building type. For exammple, a different suite should be developed for a hospital, where air change rates rule the day. 

Next, benchmarks for each of the KPIs can be pulled from an energy model or from similar systems across the portfolio. The energy model can output benchmarks for versions of ASHRAE 90.1 to determine whether systems meet or beat the standard. Here’s an example from the paper:

Finally, your analytics software can be tailored to provide the KPIs in the right format for the user. It can show KPIs when the data is available and hide them when it’s not. Or it can roll-up KPIs over a time period that makes sense for that KPI.

¹Hong, T., Li, H., Sofos, M. 2019. “System-level key performance indicators (KPIs) for building performance evaluation.” ASHRAE Transactions 125(2)


+ Lessons from Tesla’s Approach to Innovation—Tesla has been having a moment. This essay breaks down some lessons we can learn from their success.

Regardless of your views of Tesla’s future success, the company has developed a fascinating multi-pronged strategy for fundamentally changing an industry.

The core strategy has unique elements at each level of the ecosystem:

  1. overturning the core product architecture

  2. positioning themselves in key bottleneck components

  3. resolving system-level limitations that slow the adoption of the technology

At the same time, they have applied an effective approach to build their innovation capital so they can win the resources and support to execute on their vision.

There’s only one company in our industry that comes to mind here… and we’ll talk more about them next week! Oh, the suspense. 🙃


OK, that’s all for this week—thanks for reading Nexus!

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