Well, you know one thing that makes us unique...is that we have here both the
ability to model buildings and we also look at what's happening to the electric grid.
So, as we get more and more renewables on the electric grid that's a
challenge for utilities...they're used to dealing with baseload coal plants...
baseload nuclear plants...as we get more photovoltaics and wind onto the grid...
Now the source of electricity becomes variable...
And so how do we handle that?
Well, it turns out that buildings use about three-quarters of the electricity in the United States.
So, if we're careful and we control how buildings use that
electricity and when they use that electricity...we can look at the whole picture.
So, rather than just looking at the building in isolation...now we look at
it in terms of how it interacts with the nation's electric grid.
The gap that we're trying to fill - that's not solved within the private sector currently -
is the ability to tackle the next generation of the electric grid.
We define advanced analytics as the application of modeling and data science
to give us the ability to predict the performance of components and whole buildings.
A key aspect of advanced analytics is using physics-based modeling
in order to predict energy consumption.
We use high-performance computing capability here at NREL...
Namely, the Peregrine asset...
And with that capability it allows us to provide a pathway to most cost-effectively achieve energy efficiency.
Commercial building and residential building modeling use a
lot of the same underlying tools and simulation engines...and we've been
leveraging those tools to model the whole US housing stock, which we call ResStock.
ResStock is an open-source tool for modeling the residential building stock.
Whether that's nationwide or for a particular city, state, or utility service territory.
And, along with its sister tool for the commercial building stock,
it takes a new approach to large-scale building energy analysis and it does
that by combining large public and private data sources
and statistical sampling of those data sources, as well as automatic generation
of hundreds of thousands of building energy models and then it does detailed
sub hourly simulations of those models run on a supercomputer or on distributed cloud computing.
The potential impact of this capability is that we've identified
49 billion dollars per year of potential utility bill savings for consumers nationwide.
And these are home improvements that more than pay for
themselves in terms of their utility bill savings over the useful life of the improvement.
We are a National Laboratory.
We look at everything we do as providing value to the nation as a whole.
So when people come to us...
They can expect very high objectivity.
We're here to tell them the truth and help them
to design buildings that are energy efficient and also to enable them to
make this conversion to this clean electric grid.
The clean energy economy is going to happen.
And really, the decision that we're all faced with is:
Do we want the United States to lead in that transformation?
And, I believe, through our investments here at NREL, we're positioning the U.S. to lead in that space.
I think what's really exciting about this work is...
there's so much potential to make a difference.
Again, because buildings use so much energy and so much electricity...
If we can influence how much energy buildings use and when it uses that energy...
We can really enable this transition from fossil fuels to
renewable energy...we can get so much more photovoltaics on the electric grid...
wind energy on the electric grid...if we do buildings right.