True Analytics™ - Energy Savings, Comfort, and Operational Efficiency
Autonomous Systems and Cloud
This article is my musings on the publication of Barney Capehart’s book “Automated Diagnostics and Analytics for Buildings” by Barney Capehart and Michael Brambley, ISBN 0-88173-732-1, Fairmont Press. My thoughts on autonomous distributed energy are in Chapter 29, in the section on futures. (For more information read this month's book review.)
Big Data and
Cloud-based diagnostics are some of the most critical new technologies
for smart energy. Cloud based approaches win in part because they
reduce costs for data gathering and data analysis to the minimum.
Mature users can move past failure analysis to predictive maintenance,
which is catching problems before they are problems. Beyond reduced
costs, predictive maintenance can be scheduled so as to minimize the
effects on building occupants.
These promising approaches face two barriers to much wider adoption, one short term (tactical) and one longer term (strategic).
Big data lives and dies on semantic standardization, or, eventually, on ontological classification. Building systems today have only minimal alignment between the tags that name things and any standards taxonomy. As important as it is to know what something is, it is as important to know what it supports. Identical equipment may support comfort management and critical environments. The points monitored must not only be named, but aligned with the spaces they support, and the business processes those support.
In other words, we need standard ways to name things and their relationships to spaces in buildings.
The Haystack Project has advanced this notion by defining a common semantics for naming things in building controls. Haystack is an informal taxonomy, or folksonomy of building-based equipment. OBIX 1.1 introduces tagspaces so that control systems can serve up their taxonomic information, including Haystack. This pushes the semantic classification of building-based equipment into control system commissioning.
brings us closer to the Slim BIMs, to those lightweight Building
Information Models that are popping up all over. The essential
commissioning BIM is COBie. COBie provides a standard mapping between
equipment and system and space. But COBie, enmired in the politics of
international standards, has not yet adopted any common semantics.
The discussion of
creating common semantics for COBie is for another day. I think the
latest OmniClass from the Construction Specification Institute (CSI) is
part of that answer for North America.
One of the biggest costs of big data in buildings is classifying the points in the building into the clouds information model. This data commissioning is also the biggest cost for most sorts of green engineering and retro-commissioning. Reduced costs of entry will mean faster start-up and wider adoption. I hope to be able to write more about that here, soon.
In the longer term, the wider deployment of autonomous systems will present its own challenges to big data. Building systems have traditionally been naked to the systems that oversee them, exposing all points, and allowing all controls. That model is slowly being replaced.
Current trends in smart energy are encapsulating systems and negotiating on energy use rather than on controls. Energle uses a model of distributed control more common today in IT systems than in buildings. The DOE is urging adoption of Voltron agents in equipment. The OpenADR Alliance is using service oriented signals to negotiate effects rather than controls. New initiatives are developing open-source agents for distributed building management.
systems will reduce the information available to big data. Big data
will adjust, as it always does. The result will increase the benefits
of a standard ontological framework for energy using systems, placing
them firmly into frameworks of spaces supported and business purposes
served. Autonomous systems will increase the value of a standard
semantic frameworks to the big cloud.
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