Award winning manufacturer of IT-based building automation.
Dynamic Data Fuels Deep Analytics
The journey will be
driven by the first wave of online analytics that
will point to the potential of looking further into building operation
opportunities, but further analytics will be required to factually
quantify these opportunities. As we all know analytics begat analytics.
As the number of sensors increases dramatically we will be able to
explore new methods of achieving lower cost sensors; as low as $10
wireless with little or no installation. The Building Technologies
Office (BTO) at The Department of Energy is working on this now please
read this month’s interview: High Impact Technologies.
John Petze of SkyFoundry talks about the complexities of Making Sense of the Data-Oriented Tools Available to Facility Managers in this article:
Data Access – A Key Requirement for All Data-Oriented Tools
All of these tools are dependent on being able to access relevant data. Because alarms are processed locally in the control system data access is not an issue—the data “is there”. In order to take advantage of the others tools, however, we need to assess the process to gain access to the data. A good way to start an assessment is by answering the following:
1. What data do you have?
Examples: Energy meter data, facility data (size, location, type, year of construction, etc.), equipment operation data such as on/off status, sensor data, etc.
2. Where is the data located?
Examples: BAS system, SQL database, utility company website, Excel spreadsheets, etc.
3. What method will be used to access it?
Examples: Live collection of data via Bacnet or oBix, Haystack, Modbus, etc., data download from utility website via xml (perhaps Green Button data), CSV file import, SQL queries processed on a daily, weekly, hourly or minutely basis. The answers to these questions will vary dramatically based on characteristics of the specific project and customers needs.
A Fast Moving Field
None of the examples presented are meant to be absolute, rather they are offered to help systems integrators and facility managers gain an understanding of these tools, their requirements and potential benefits. With the rapid advances in data-oriented facility management tools, there is overlap between them and the lines blur as vendors advance their technology.
Another key point to consider is that you don’t have to “do it all” to get value from data analytics. While you can’t install half of a chiller, you can start with analytics on a small subset of your data. Energy data, building occupancy, and weather is a great place to start.
In this article Jim puts technology second... interesting! Here is Smart Building Strategy: Tackle Behavior First, Technology Second by Jim Sinopoli, Smart Buildings, LLC.
Existing wireless devices such as our phones, tablets, and computers will become identifiers and roll call devices announcing our presents in the building. Jim Sinopoli's article discusses how these devices have the ability to change our habits.
It’s no secret that technology changes behavior, and that’s likely to be just as true with buildings as it is with, say, mobile devices. But while we often think of the latest technology as a way to improve building functions, we usually fail to fully consider how technology might alter the way people use or manage those buildings. In other words, we tend to overvalue the role of the technology and undervalue the resulting re-engineering of the operational processes. And it’s those process changes, rather than the technology itself, that can actually have the biggest impact on costs, effectiveness and efficiency.
To demonstrate the relationship between technology, behavior and performance, here are a few examples of smart devices that use automation technology to change, control and adapt behavior.
Meanwhile, Sam Grinter, HIS, poses this question in Could Legislation Drive Growth in Building Analytics?
A new report by IMS Research – now part of IHS Inc. (NYSE: IHS) – has found that legislation could be the deciding factor if building analytics are to see widespread adoption.
Building analytics are one of the hottest topics in the industry at the moment however; the scale of deployments has been relatively small. While the industry hype has been a key factor in educating potential customers about the benefits of using building analytics, alone it will not persuade organisations on mass to invest in what is a relatively new and untested technology.
One of the greatest challenges building analytic vendors must overcome is justifying investment. Building owners have been maintaining their buildings quite happily for decades without the need for building analytics, so a clear incentive for adopting building analytics has to be presented.
Many building analytics vendors state that their solutions can save from 10% to 30% from energy bills. However, without a more accurate estimate of exactly what the savings would be, it will be difficult to convince a potential customer to invest in building analytics on a large scale.
The challenge for building analytics to gain widespread adoption is for a clear incentive to be introduced that prompts buildings owners to install these solutions.
So what needs to change for building analytics to see widespread adoption?
Legislation could be key to creating a real incentive for companies and governments to adopt building analytics.
Existing legislation in many countries requires new or renovated buildings to display an energy certificate. In 2009, New York City introduced more in-depth legislation, that requires all commercial buildings over 50,000 ft2 to benchmark energy performance and publicly disclose the results. In Australia, a carbon tax introduced in 2012 forces polluters to pay per tonne of carbon released into the atmosphere. Both examples of legislation incentivise building owners to reduce energy consumption and increase energy efficiency.
As a consequence of the introduction of the carbon tax in Australia, there has been strong interest and adoption of building analytics. IHS estimates the Oceania region (Australia, New Zealand, and New Guinea) was the second largest market for building analytics in terms of revenue in 2012, after the U.S.
To achieve “Deep Analytics” we need to strive for mutual toleration, community diversity admiration, and collaboration of the connection community.
More on this next month.
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