IBM Research solar camera watches for,
predicts solar energy
The best solar panel only converts about
20 percent of the sun’s rays hitting its surface into usable electricity. On a
perfect day at sea level, that panel could generate approximately 200 watts of
electricity per square meter. Introduce clouds, shade from trees, or dust in
the wind and that power drops even further – making solar a variable energy
source for the grid, or anything else powered by photovoltaic panels. So, our
physical analytics team at IBM Research built a basketball-sized and shaped
camera that can predict solar radiation for the Department of Energy – and more
recently, the University of Michigan’s solar car team. The wide-angle (more than 180 degrees) sky
camera provides information on sky conditions and their rate of change. Where is the sun? How many clouds are in the sky (and with some triangulation with other cameras, how high are they)?
The sky camera is also built with a solar
pyranometer, GPS sensor and onboard processor, all packaged in a weatherproof enclosure for external use. The pyranometer provides an instantaneous and
precise readout of solar radiation. The GPS sensor give precise location information. All of this information is processed using an onboard computer and
a predictive model to generate real time solar insolation (sun exposure) values and prediction for 10-15 minutes in the future. These results are transmitted
by direct network connection, or satellite link in the case of the mobile unit.
The sky camera data is sent to a cognitive computer that couples this real time information with incoming
weather data, such as wind speed and direction, as well as historical
forecasts. The machine learning algorithms that interpret this information give
us an improved picture of how much solar energy our panels are absorbing now,
and how soon-to-happen weather events will affect the panels’ ability to absorb
the sun’s rays.
Knowing how much solar energy we will or
won’t have in the next several minutes gives a grid operator time to shift
energy resources. For example, our recent Department of
Energy-funded tests with “ISO-New England, the grid operator serving
Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island and Vermont,
show that our system can be 30
percent more accurate than other state-of-the-art approaches.”
On
the move with the University of Michigan
Utility grids are stationary. Put the
camera in a place with an unobstructed view of the sky, and let the data roll
in. So, when the University of Michigan’s solar car team asked us about using
the cameras to help with forecasts for their upcoming World Solar Challenge
race in Australia, we had to not only think about all the variables of it continuously
seeing and understanding a new slice of sky as it cruised down the road, but
also how we were going to make it mobile at all.
The solar car is a four-wheeled
solar panel that can maintain speeds well above 60 miles per hour. And we would
need to know at any moment the car’s solar absorption per square meter of its
panel. A rental car’s roof luggage rack is not a good fit!
Thankfully, after a 600 mile mock race
with the UM students, the mounting system is stable and ready for the
Australian outback on October 18.
Our camera will be perched on top of UM’s
weather car, which drives about 10 to 15 minutes ahead of their solar car. This
allows the camera to absorb data from the approaching sky, and our system to
produce a short term forecast, for the UM team. If clouds are moving toward the
route, they can determine if the solar car should change its speed based on the
clouds’ speed – saving valuable time and energy.
The system will also use the camera’s data
and historical forecast data to produce a long range forecast. This prediction
gives UM a benchmark to help decide where to park the solar car during the
week-long race. Where and when the team stops will make an important difference
in how efficiently they can recharge the car’s battery via the solar panels
before nightfall.
Only three hours separated first and
second place at the 2013 World Solar Challenge. We hope our camera and system
help UM save as many precious minutes as possible. We also hope that this race
will demonstrate cognitive computing in solar forecasting. Large solar facilities,
like ISO-New England, can quickly shift Gigawatts of power to other sources
when a thunderstorm rolls in, and know when to switch back without any
downtime, or worse, outages.
Read more about the World Solar Challenge and solar forecasting
Editor's note: this article was contributed by Senior Technical Staff Member Theodore Van Kessel, Research Staff Member Xiaoyan Shao, and Research Staff Member
Siyuan Lu of IBM Research's Physical Analytics team.
The system will also use the camera’s data
and historical forecast data to produce a long range forecast. This prediction
gives UM a benchmark to help decide where to park the solar car during the
week-long race. Where and when the team stops will make an important difference
in how efficiently they can recharge the car’s battery via the solar panels
before nightfall.