National and regional climate data are derived from single point local
observations. NOAA-EDS archives such local obs which have fairly standard
obs site layouts to provide for consistency.
However, site selection for new solar energy acquisition systems must
account for local effects such as shading from trees, obstructions from
buildings and from mountains and valleys.
Topology and surface characteristics are important for wind turbine site
selection. For example, winds off the northeast coast of the US are much
better for turbines than winds off the southeast coast. Funneling effects
due to terrain for winds such as west Texas and near Palm Springs CA are
characteristically excellent for turbine sites and, wind turbines have been
built in those areas years ago.
WeatherBug lets users choose SI or conventional unit readout.
Weatherbug also includes obs from local schools and other locations to
provide a very comprehensive data set. Schools use the data for teaching
purposes. Bob Ryan, former President of the American Meteorological
Society, was a driving force in expanding the school weather observation
program to help expand interest in science for kids.
Stan Doore
----- Original Message -----
From: "James R. Frysinger" <[email protected]>
To: "U.S. Metric Association" <[email protected]>
Sent: Saturday, September 19, 2009 7:27 PM
Subject: [USMA:45847] Re: Weather data for solar and wind power calculations
Indeed, data is available on national and regional scales that can
indicate in general the feasibility of establishing solar and wind
energy farms. However, they suffer a bit due to their course
granularity. That is a more significant concern for wind farms, where
local topography can create large divergences from the predictions due
to lee and funneling effects.
My site is in a Category II region, which is generally not favorable for
capturing wind energy. My local data happens to support that at the
location of the weather station for the time period of the data I
reported. I have since moved the station to a site with no lee effects,
so the data since then might be slightly better. But I doubt that it
would show a wind turbine to be feasible even there.
One of the things I like about WeatherLink is that it reads out and
archives mostly in SI units. There are a few exceptions. Insolation is
reported in watts per square meter (W/m2) but solar energy is reported
in langleys (1 La = 41.84 J/m2). UV dose is reported in MEDs (1 MED =
125 mJ/cm2 = 1.25 kJ/m2). (MED stands for Minimum Erythemal Dose.) UV
intensity is reported on the UV index scale used by the World
Meteorology Organization. Of course, once I pull the data files up into
a spreadsheet I can create columns that convert the UV dose and solar
energy columns to read in SI units.
I have a couple of other nice features available. WeatherLink uses an
algorithm to calculate evapotramspiration (in millimeters). Comparing
daily rainfalls and evapotranspiration numbers provides indication
(within the limits of the algorithm) on the net gain or loss of water to
the soil. I also have a second thermometer outside that provides soil
temperature. Since my main use for this weather data is agricultural in
nature, I have the soil thermometer 10 cm below the surface and the
anemometer 2.0 m above the surface of the soil. Were I actually
interested in erecting a wind turbine, I would be better off sampling
the wind at an elevation 10 m above soil level.
I might point out that our new house, being built next to our cabin
here, has a ridgepole orientation that is essentially E-W. At some
future date we or our heirs could mount solar collectors on its southern
face.
Jim
STANLEY DOORE wrote:
Climatology for locating wind turbine systems is well-established and
published.
Similarly, climatology for solar isolation is readily available and, it
includes cloud cover as well as for clear skies.
These data should be used in planning and locating energy collection
systems. The best type of system should be selected based on these
available data.
The type of solar energy collection system - roof top/distributed or
concentrated/centralized - are in competition. Each has advantages and
disadvantages.
However, there is a hugh number of roofs which can be covered with
thin-film solar photovoltaic collectors (panels) which would provide
direct input to the building and, excess electricity could be fed back to
the grid. This would eliminate transmission loss and not take land space
that huge concentrated solar energy collection systems would.
Thin-film solar collector and installation costs are dropping rapidly
and, they can be located where wind farms are not practical. However,
wind farms can generate electrical energy where solar systems are not
practical.
In short, climatology plays a significant role in types used for
locating energy generation. In some places, both types may be
cost-effective.
Data are readily available from the National Oceanographic and
Atmospheric Administration's (NOAA) Environmental Data Service (EDS).
The International System of Units (SI) should be used exclusively to
make comparisons among the various systems to simplify understanding.
Stan Doore
----- Original Message ----- From: "James R. Frysinger"
<[email protected]>
To: "U.S. Metric Association" <[email protected]>
Sent: Saturday, September 19, 2009 11:14 AM
Subject: [USMA:45845] Re: Weather data for solar and wind power
calculations
One sees occasional anomalies in "raw" data, John. Yes, that one
instantaneous insolation number is nearly the theoretical maximum at the
top of the atmosphere AND at the Sun's declination. I have not searched
the table of numbers; that value might have been recorded at night! It
is impossible to say what caused the instrument to record that
particular value: a reflection, electronic noise, a flash of lightning,
...
The data does show that our farm would be a very poor candidate for
harvesting the wind. Just looking at the large fraction of time in which
the winds are below or just at minimum turbine needs dissuades such
notions. Our low solar average insolation likewise does not encourage me
to erect large solar arrays to ease TVA's load, but of course it
suffices for powering remote electric fences.
Jim
John M. Steele wrote:
Interesting data. Your solar sensor may be a bit "optimistic." The
maximum value is essentially equal to the accepted value for solar
incidence at the top of the earth's atmosphere. At sea level, for
overhead sun the value is usually taken as 950 - 1000 W/m². Since it is
never "overhead" at your latitude, the number would be slightly lower.
(If you were on top of a substantial mountain, it could be a little
higher, but not at your altitude).
On the wind data, even your highest 10 minute value would put you well
down in the cube law region and at roughly 7.5% of rated power (usually
generators require 12 - 14 m/s for rated power).
--- On *Fri, 9/18/09, James R. Frysinger /<[email protected]>/*
wrote:
From: James R. Frysinger <[email protected]>
Subject: [USMA:45842] Weather data for solar and wind power
calculations
To: "U.S. Metric Association" <[email protected]>
Date: Friday, September 18, 2009, 10:46 PM
I have a Davis Vantage Pro2 weather station and console, read out
and archived with WeatherLink software.
Here is some actual weather data that might interest those concerned
with harnessing solar or wind power. The data was collected on my
farm in Middle Tennessee. To four places, the weather station
location then was 35.9785N, 085.5087W; it has since been moved to a
better, nearby site. This old site was somewhat shielded by nearby
buildings, but still the data is interesting (at least to me).
The data is collected every few seconds and recorded in 10 min bins.
Each bin's value then represents an average over that 10 min period,
but a few channels also record "instantaneous" high and low values
during that period as well. I have pulled the 52599 bins (10 min
each) of data that I obtained for 2008 into a spreadsheet, in which
I have started some additional statistical analysis.
My data follows, without regard to significant figures. Note that
wind speed data is sensed and transmitted in 1 mi/h intervals. The
logger is set up to convert those to kilometers per hour and it then
rounds it to the nearest 0.1 km/h. Thus wind speeds are archived in
1.61 km/h intervals to the nearest 0.1 km/h.
Solar (essentially no effective shadowing by nearby structures):
minimum insolation 0 W/m2
maximum insolation 1180 W/m2 (10 min average)
maximum insolation 1331 W/m2 ("instantaneous")
average insolation 165.02 W/m2 (10 min average)
Wind (two one story buildings in the south to northwest sector):
minimum speed 0 km/h
maximum speed 20.9 km/h (10 min average)
maximum speed 57.9 km/h ("instantaneous")
average speed 1.66 km/h (10 min average)
Wind bin data:
speed freq. log frequency
(km/h) 0.0 27658 4.44
1.6 10401 4.02
3.2 6681 3.82
4.8 3920 3.59
6.4 2033 3.31
8.0 1036 3.02
9.7 488 2.69
11.3 218 2.34
12.9 90 1.95
14.5 42 1.62
16.1 21 1.32
17.7 7 0.85
19.3 3 0.48
20.9 1 0
The best fit straight line for the log frequency data is
log f = 4.55 - 0.21v/(km/h)
Those who love to play with numbers can cube the speeds (I recommend
converting to m/s first), then doing a numerical integration using
the above frequency data to determine the mean power available in
the wind here. Or the regression equation above can be used to do
the integration if you're a hard-core mathematician. Either some
more information will be needed.
The elevation here is 375 m above sea level. The average sea level
pressure (obtained by altitude correction to sea level by the
logger) was 1017.8 kPa (min 981.3 hPa, max 1046.4 hPa). The average
relative was humidity of 71 % (min 17 %, max 98 %). The average
temperature was 14.54 °C (min -13.4 °C, max 35.0 °C). These are all
10 min bin averages. You will need these numbers to correct to
actual barometric pressure and to calculate the density of air here
(on the average, sort of).
Hopefully this will give someone some interesting real-world numbers
to play with.
Jim
-- James R. Frysinger
632 Stony Point Mountain Road
Doyle, TN 38559-3030
(C) 931.212.0267
(H) 931.657.3107
(F) 931.657.3108
--
James R. Frysinger
632 Stony Point Mountain Road
Doyle, TN 38559-3030
(C) 931.212.0267
(H) 931.657.3107
(F) 931.657.3108
