CORM 2010 - Solar Data Warehouse

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2010 CORM Conference
Dr. James Hall
JHtech
Nature’s Solar Power Plants
Agriculture Needs Better Solar Data for Modeling
Solar Radiation Measurements
Satellite-based of solar radiation data:
• Near-Real time
• Provides Global Coverage
However:
• Cannot distinguish between clouds and snow cover.
• Measurements are less accurate near mountains,
oceans or other large bodies of water.
• Measurements are made at the top of the atmosphere
and require models to estimate the solar radiation at
the ground.
Solar Radiation Measurements
Accuracy of solar radiation data:
• Highest quality research sites: 3-6% error
• Routine operational ground sites: 6-12% error
• Satellite observations: 20% error based on NASA
estimates (35 W/m2 RMS)
• Satellite observations: 19% error based on third party
estimates
Ground-based measurements are clearly
more accurate than satellite data
Satellite Measurements
20 % Error
Average Error of Satellite Observations
RMS error of 35W/m2 = 0.84 kWh/m2/day
Solar Radiation Measurements
National Solar Radiation Database:
• Measurements from only 40 high-quality stations,
remaining 1414 locations were modeled.
• Not real-time, latest series is 2003-2005.
• Accuracy not published by NREL
• Intended to be statistically representative, not
historically accurate!*
* User Manual for National Solar Radiation Database
Solar Data Warehouse
Largest agro-climate database:
• Hourly & Daily data for last 5-20 years at 3000+ US locations
• Soils, Weather, Evapotranspiration, Solar, Soil Temp
• Multiple Layers of Quality Control
• Near real-time
• Lowest error of any national solar radiation source
Solar Data Warehouse
Hourly & Daily data on temperature, precipitation, humidity, wind speed, solar
radiation, evapo-transpiration for 3000 US locations. Soil temperature is also
available for many locations.
Solar Data Warehouse
SDW data shows much greater discrimination of solar variations
Solar Data Warehouse
Our Data Sources:
• Over 30 different networks across the US.
• Run by federal agencies, states and
universities for their own specific purposes
• Many different formats & no bulk access
• Medium-quality sensors
• Little or no quality control
Relative Accuracy
Relative Accuracy
Using measured data from 10 locations from the Solar Data Warehouse as the
baseline, we calculated the Average Daily Error in the National Solar Radiation
Database. We also calculated the average error for a second, nearby station from
the Solar Data Warehouse
Under Development
• Improved Solar Forecasts
• In-season Crop Growth Models
• Unique Solar Atlas
Self-Improving Forecasts
Instrument
Panel
• Combine published
techniques for cloud &
climate modeling
• Compare forecast to
actual solar radiation
Forecast
Grid
Feedback
Correction
• Feed back error based
on near-real time station
data
Crop Growth Models
Detailed Climate &
Soil Data
Crop Physiological
Growth Model
Accurate
Yield
Forecast
• 15 years ago, researchers demonstrated accurate yield
forecasts by modeling day-to day crop growth.
• Past models have only proven accurate for a specific
region.
• Accurate, near-real time data on Solar Radiation has
always been a limiting factor
Crop Growth Models
Detailed Climate &
Soil Data
Crop Physiological
Growth Model
Regional
Adjustments
Accurate
Yield
Forecast
Grower
Adjustments
• Artificial Intelligence technology is used to discover the
correct parameters for different regions and grower
practices.
• Currently developing calibrated corn growth models for
all counties in the US.
Unique Solar Atlas
Solar-Atlas.blogspot.com
Near real time using measured data from 3000 stations
References and additional information on the material in this presentation can
be found at: http://www.solardatawarehouse.com/WhitePaper.pdf
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