[LDES-coremodel] NSRDB data for California

[russ.jones at ieee.org]

Sarah and team,

 

I have finished downloading the NSRDB data for California for 4310
locations on a 0.1°×0.1° grid, and you can find it in the “NSRDB” folder at
the following link:


<https://jonesse-my.sharepoint.com/:f:/g/personal/rkj_jonesse_onmicrosoft_co
m/EnPMb0wL_c1KvAac76FGDqMB_MPXQeOXuJtGRvfKZSWQ6g?e=JhIZ20>  CA_data

Each location has been rolled up into one csv file with 23 years of data,
and also csv files 23-years aggregated daily, monthly, and yearly values.
The hourly data is 90GB but there’s an alternate folder where each file is
compressed that is reduced to 21 GB.  The aggregated files also have heating
degree-days (hdd) and cooling degree-days (cdd) calculated.

 

There are also a bunch of shapefiles that I used with geopandas (thanks
Pedro!) to classify those locations according to county, forecast zone,
census tract, etc., and there’s a sort of master file “CA_latlon.xlsx” (or
(“CA_latlon.csv”) that contains a list of the (lat,lon) coordinates within
California and those classifications, along with population per cell and
aggregated values from the NSRDB.  There’s a plotting program called
“quickmap.py” in my repository (KurtzGroup/russ_tools) that will make a
quick map (!) of any of the columns. Here are some examples (log of
population per coordinate because a linear plot is really boring):



           logpop2020                                         hdd
cdd                                                  hdd+cdd

 

I wrote a script to calculate statewide-aggregated daily, monthly, and
yearly values — with the temperature, hdd, and cdd values weighted by
population because the load is where the population is.  I’ve attached a
spreadsheet showing how each of the variables has varied over the 23-year
history.  What kinda jumps out from this is how heating degree days are
systematically declining. 

                                     hdd
cdd



The numerical shading is reversed on these so that in both cases red =
hotter. You can discern from this that heating degree-days are being lost
faster than cooling degree-days are being added. That’s consistent with the
findings of Pierce and Cayan from Scripps Institute, who based their
findings on analysis from future climate models.

 

The bright side is that the load is shifting to less in winter and more in
summer and thus can be more readily satisfied by solar. Also, we won’t need
to take up all that closet space for jackets and sweaters.

 

Russ Jones

Jones Solar Engineering

Visiting Scholar — Center for Energy Research, University of California at
San Diego

Senior Member, Institute for Electrical and Electronics Engineers (IEEE)

+1-714-206-2556 (m)  ☼   +1-310-469-9045 (VOIP worldwide)

 

 

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