January 2013
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AutomatedBuildings.com

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Green Button – The Universal EMS

And it’s free

Dave Krinkel
Dave Krinkel
Founder
EnergyAi™

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It seems like every article about the latest energy management system or building control system starts with something like “there are five million non-residential facilities in the U.S., which spend over $200 billion a year on energy”. The implication of course is that the whiz bang technology described in the article can take a big bite out of that energy spend.

But the vast majority of those buildings are home to small organizations like the local diner, hardware store, church, community center, etc. Their individual energy spend is small, they don’t have a dedicated facility or energy manager, and their most sophisticated building controls for the foreseeable future are a programmable thermostat and light switches.

However, thousands of these small businesses and organizations do have a basic but powerful energy management system, which their electric utility gave them for free. This system can reveal simple operational changes which will reduce their electric bill. I’m referring to smart meters and the “Green Button” initiative.

Think of smart meters as the data collection part of an energy management system. They measure energy use and other variables every few minutes, and upload that data to the utility. The Green Button is a standard method to make this data available to the customer for free, in a standard open source format, literally with the click of a button on the utility’s web site. Seven major U.S. utilities have already implemented the Green Button, and 26 others have announced plans to do so (see http://www.greenbuttondata.org/greenadopt.html for a full list). Combined, they will provide over 27 million customers with up to 13 months of past energy use at hourly or finer intervals.

The data provided by smart meters and the Green Button is not new. What’s new is the scale of availability and the price (free). Until now, most utilities only provided this information to their largest customers for a fee.

Reliable Controls But now this detailed use history can be downloaded by energy users of any size, including small and medium businesses. They don’t have the time or expertise to analyze their history. The idea that this data might be of value is new. It faces a significant “show me” hurdle.

The traditional approach to extracting value from historical use data is visualization. Visualization operates on the principle that if you look at your usage in the right way, you often find unexpected patterns which lead to savings opportunities. But this is a time-consuming process. You must look at your usage from a variety of perspectives, and interpret the results.

To extract the full value from this data explosion, we need to go beyond visualization. We need automated tools which find important use patterns and anomalies, and present the findings in concise, clear language, not just charts. And at a price point affordable to the smallest energy user. Here are some examples of going “beyond visualization” to provide simple actionable information to a customer:

Typical Patterns

Energy usage for most facilities follows recurring patterns. Certain days of the week (like Mon-Fri) may be higher than others. The load tends to “wake up” and “go to sleep” about the same time each day.

But these patterns are often not what the building owner or occupant expected. A recent analysis of Green Button data for a medical office building revealed that the load was ramping up much earlier, and dropping off much later than necessary:

Typrical Patterns

•    Load rises from 3am to 5am on 73% of Mondays-Fridays
   •    Load drops from 7pm to 10pm on 91% of Mondays-Fridays

The bullet points in this example highlight the most important information in the chart of a typical weekly load profile.

24x7 Demand

The historical record not only tells you when your facility goes to sleep and wakes up, it can also tell you how “deeply” it sleeps. Demand from equipment which runs 24 hours/day, 7 days/week is often higher than expected.

An analysis of meter data for a municipal parking garage revealed a load shape similar to a data center. Equipment operating 24 hours a day comprised over 70% of total electricity use.

24X7 Demand

•    74% of total electricity use may be from equipment which is always on
      •    A 10% reduction in this demand would save an estimated $22,000 per year

The importance of the 24x7 demand for a specific facility can be powerfully expressed in two ways. First as a percentage of the total annual electricity use. The second metric is to show the impact of even a small savings. An analysis of this type cannot specify what is creating the 24x7 demand, but it can estimate the savings from lowering it by even 10%.

Unusually High Use

Green Button data is not “real-time”. So it can’t be used for real-time alerting or fault detection. But you can learn a lot from “historic fault detection”. Once the typical patterns for a facility are known (as described earlier), you can identify times when energy use was significantly higher or lower than expected. And often there are patterns to these spikes and dips.

In some facilities for example, unexpectedly high use occurs most often during late night-early morning hours:

Unusually High Use 

“Demand was higher than expected for 400 hours during early morning hours (1am – 5am).
Avoiding this excess use would have saved an estimated $6,000.”

Apps for All Energy Users

So the challenge and opportunity is enabling owners and occupants for literally millions of facilities to extract the “nuggets” in their historical energy use data. Here is where the concise nature of “beyond visualization” findings is a big advantage. Imagine uploading your Green Button data to a cloud-based analysis service, and in a few seconds accessing the following on your smart phone:

 Apps for All Energy Users



About the Author

Dave Krinkel is the founder of EnergyAi (www.energyai.com), based in Berkeley, CA. In 1978 he was a researcher in the pioneering Energy Efficient Buildings team at Lawrence Berkeley National Lab. For the last 25 years, Dave has developed energy analysis tools for utilities, ESCO’s, and a wide variety of industrial, commercial, and institutional end users. Prior to EnergyAi, Dave held senior positions at Itron, Silicon Energy, and SRC Systems. He can be reached at dkrinkel@energyai.com.

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