IEEE Power & Energy Magazine - November/December 2015 - 57

The U.S. Department of Energy (DOE) is currently funding several efforts to improve the underlying models
for solar forecasting under its SunShot initiative. This
includes research by IBM on optimal blending of different models using machine-learning strategies and by the
National Center for Atmospheric Research (NCAR) on
combining individual models including sky imaging, satellite methods, and a solar-tuned NWP model to maximize
the benefits of the individual methods. NOAA is adding
various solar energy-related parameters, such as outgoing
longwave radiation and incoming shortwave radiation, as
well as direct and diffuse irradiance, to its hourly updated
Rapid Refresh models. Efforts are also ongoing in Europe,
including ones focusing on improving fog, snow, and dust
representation in NWP models (for example, the MACCII
project), and in academia, such as a University of Arizona
project to develop a hybrid forecasting system at a high
time resolution.

Incorporation of BTM into
Load Forecasting Methods
The load forecasting community is evaluating different ways
of incorporating BTM PVs into existing load forecasts and
the costs and benefits of using more detailed weather and
PV system data, where available. In California, a number
of government funded efforts are underway, using information about PV installations to predict the day-ahead and
hour-ahead power output of the fleet. Clean Power Research
has worked to integrate its solar forecasts into Itron's load

MAE (% of Capacity)

Atmospheric Modeling Enhancement
and Blending of Methods
20
18
16
14
12
10
8
6
4
2
0

Annual Day-Ahead Solar Power Forecast MAE
Versus Forecast Target Entity Capacity

1

10,000
10
100
1,000
Capacity of Forecast Target Entity (MW)

figure 7. The relationship of the annual day-ahead solar
power forecast MAE (% of capacity) over all daylight hours
to the installed capacity of the forecast target entity for a
broad spectrum of entities (individual facility, regional, or
system-wide aggregate).

forecasting tool, focusing on improvements to BTM forecast
accuracy and integration of direct power prediction into the
neural net load forecasting framework.

Cloud Propagation Techniques
Sky imaging and satellite-based cloud propagation methods are
still at early stages of development, and several research groups
are actively working to improve them. For instance, Colorado
State University is developing a satellite-derived insolation

8
7

Fall and Spring:
Larger Errors Due to Fog and Snow
with Medium Production Level

(N ) RMSE

6
5
4
3

Summer:
Small Errors
Despite High Production Level

2
1

Winter:
Small Errors Due to
Low Production Level

0
Mar. 2014

July 2014

Sep. 2014
Time

Nov. 2014

Jan. 2015

Mar. 2015

©energy & meteo systems

figure 8. A day-ahead forecast RMSE by month for distributed and centralized solar power in Germany, showing the
impact of fog and snow.
november/december 2015

ieee power & energy magazine

57



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