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|The Story Threads to Follow
as Smart Buildings Enter a New Year
The storyline that emerged in 2016 offered new clues and revealed certain dead-ends.
arrived at the end of another year, and the mystery of when
adoption of Smart Building concepts will truly take off continues. The
storyline that emerged in 2016 offered new clues and revealed certain
dead-ends. Here is my plot summary of the who, what, where, how and why
as we enter 2017.
Who: Data Wranglers
The building controls industry is a central player on
the data landscape today. The open-protocol
community of the industry has welcomed bright technical talent from the
ranks of HVAC family businesses, energy management start-ups, property
management firms, building equipment corporations and other wandering
paths. Our systems integration knowledge has expanded along with the
evolution of web services and data transport methodologies. Our people
know how to gather and structure building operational data for
analytics, how to store it, and when to do batch versus real-time
processing. That is data engineering and/or data wrangling according to
this article from the Insight Data Science Fellows
Program. But, we don’t often call it that. Could 2017 be the year
we sync up a vocabulary with the rest of the data industry?
A great conversation about being welcoming and inclusive to a diversity of people –especially young people looking for a purposeful career–is available from the ControlTrends Network CTN. Download this podcast hosted by ‘Young Guns’ Rob Allen and Brad White as they interview guest Lindsay Baker, CEO of Comfy. They talk about how industry jargon can act like a wall keeping newcomers out. Certainly, the data world has its own impenetrable language. But data wrangler is a friendly, approachable term to use more in 2017.
What: Cyber-Physical Systems
As AutomatedBuildings Editor Ken Sinclair often explains
the controls industry has been going about the business of digitalizing
the physics of building operations ever since the first Direct Digital
Control (DDC) devices were introduced as a replacement for pneumatics
decades ago. But, in 2016 he noticed that the pace of that process was
going from evolutionary to revolutionary. Almost every month of
2016 brought new announcements about ever more intelligent, powerful
and versatile control devices for building systems. Edge Analytics Controllers (EACs) introduced in
March by BASSG were among the first that he recognized as a brand-new
category. “They have the brains of a smartphone and a chassis that can
plug right into a control panel,” is how he describes them in this CTN podcast.
What to call the new category? He settled on “Maker Devices” in his October/November editorials. But, I think we will hear a lot more about Cyber-Physical Systems (CPS) in 2017. As you can read in the White House press announcement, the National Science Foundation’s Smart & Connected Communities initiative announced $4 million in new Cyber-Physical Systems (CPS) awards last September. The NSF defines Cyber-Physical Systems as:
The controls industry has put the physical in cyber-physical for a
long time. We won’t be cyber bullied. As Ken says in the podcast
referenced above, “We’ve been supplying these boards for 30 years. We
figured out what makes an input/output go on and off without the
computer telling it too.”
Where: The Acephalous Network Edge
Acephalous means ‘without a head’ and has been used to describe societies without a permanent leader or chief. The computing/communications architectures of tomorrow are going to be non-centralized, distributed, self-organizing—i.e., acephalous, said a commenter in response to a new essay by Glen Allmendinger, President of Harbor Research. He was referring to this passage from The Failure of IoT Platforms.
In August of 2016, Alper Uzmezler and I made much the
same case in our article Data Flow Will Mirror Air Flow in the Era of Hybrid
Edge Controllers. We say:
How: Open Software, Standard Meta Tags, Readily Available SDKs
Allmendinger’s essay also complements a new whitepaper from Harbor Research underwritten by
SkyFoundry: The Future of Smart
Systems and IoT Analytics. Both make the point that “the IoT
platforms that have flooded the market are data traps and information
islands.” They delve into why the client-server model of computing has
served to prohibit true data interoperability and why this rigid
hierarchical way of thinking needs to break down to enable the next era
of machine learning to come into being.
Once again these points are consistent with those Alper Uzmezler, and I made just a little earlier with our November article, Today’s Smart Building Data Exhaust Maybe Tomorrow’s Machine Learning Gold. We also warned about the potential repeat of the Protocol Wars that have kept building equipment data in walled gardens—this time with cloud application vendors holding building operational data for ransom. The best strategy is to build on open-source software to the largest extent possible and to ensure that any cloud vendors collecting your real-time data and analyzing trends maintain useful software developers’ kits so that you can get the data when you need it.
Another guideline for building data wranglers that want to steer clear of data silos is to get educated about meta data strategies. As Scott Muench describes in his July article, The Strategy and Payoffs of Meta-Data Tagging a system like the Project Haystack methodology offers a way to add meaning to data that will be understood across the design-build-operate cycle and for years to come. Project Haystack brings the power of an open-source community to the data modeling challenge. It encompasses the combined experience of system integrators with long experience combining data from different sources and bringing it into value-added applications. In 2017, Project Haystack is holding its bi-annual conference, a not-to-miss event.
Why: Machine Learning
In 2016 property industries continued to awaken to the fact that data is a valuable commodity and that buildings generate a lot of it. We are starting to see building owners and property management firms compete for tenants on the strength of their data platforms. Tenants want to lease space that supports the business-related digital services, personalized space and comfort, and energy efficiency goals they have in mind. This competition is leading the buildings industry in the same direction that manufacturing, transportation, retail and other industries have been headed for a while—toward machine learning and eventually artificial intelligence.
As Alper and I outlined in our September article When Will Machine Learning Reach Smart Buildings, “ML algorithms have advantageous self-correcting behaviors that will be the best navigators of a digitized world. But, these come at the price of being more complex to understand and work with than, for example, rule-based analytics programs. And they require a continuous and ample supply of structured data to deliver any meaningful results.” The property owning and management companies that, in 2017, put in place data strategies that will keep them in command of their data and help them get the most predictive value from it will be best positioned in the coming age of autonomous cars, smart cities, and the smart grid.
It feels like the large and small vendors and customers in the building operational data analytics software market understand where this is all heading. But size doesn’t lend the same advantages when delivering products and services to the acephalous network edge. (By the way, I don’t think acephalous will be a buzz word in 2017, or ever.)
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