Energy, Big Data, and Bungee Jumping

After you are securely strapped into a harness and walked to the edge of the platform, what is the last thing you want to hear before stepping out into free space:

“There is a greater than two sigma probability that the bungee cords won’t fail given the inputs of your weight, number of system utilizations, ambient conditions, and the mean time between failure of the elastomers used.”

In other words, you would have a 5% chance of plunging to your death. Simply saying that clearly and succinctly would clarify the risk of using large rubber bands to restrain your fall before you leap from the ledge. You could rationally decide about the sanity of your next move without taking out your calculator.

Burying the “lead” in an obfuscating (if technically correct) data analysis methodology provides a liability “cover” for the bungee jump service provider. While the exponential growth of data has no limits, our ability to effectively use that data does. Customers (who probably have a wealth of other things on their minds) will be potentially exposed to more risk than might be acceptable.

Now take the energy industry, where every report is rife with statistical overload. Nowhere more so than in the measurement and verification methods applied in determining energy efficiency reductions.

At DemandQ, our goal is to clear up this statistical “fog.” We have accumulated 40,000+ months of billing data. We interoperate with and manage peak demand and consumption for over 43 million square feet of commercial building space. We routinely gather tariff and billing information from 245 electric utilities across the US.  

In other words, we have already gathered and crunched the data.  

Some keen (obvious) insights: Every building is unique as regards the efficient use of electricity. Even properties that are built the same way from location to location will respond to ambient weather conditions in significantly different ways. The HVAC appliances installed at a site will display a variable range of performance, especially as they age and are serviced.

So, we keep it simple for property owners, energy experts, facility managers, and sustainability professionals. Based on the data we have assembled over the past 9 years, we can predict within a reasonable range what we can achieve in net savings for our customers.

We model your properties, inputting factors like size and location, average ambient conditions, utility tariffs, usage (retail, office, light or heavy industrial) and the percentage of operating cost represented by the mechanical systems cycling at your site.

Then, we propose a simple business proposition: How many devices will we need to manage to reduce your costs, and how large/complex is your property? With those two inputs, we can tell you the flat cost of our service. It will be a small percentage of the savings that you get to keep.

No over-stated savings, no financial bungee cord metrics bouncing around, and no risk.

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