Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts
Clouds are the dominant source of small-scale variability in surface solar radiation and uncertainty in its prediction. However, the increasing share of solar energy in the worldwide electric power supply increases the need for accurate solar radiation forecasts. <br><br> In this wo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-03-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://www.atmos-chem-phys.net/16/3399/2016/acp-16-3399-2016.pdf |
| Summary: | Clouds are the dominant source of small-scale variability in surface solar
radiation and uncertainty in its prediction. However, the increasing
share of solar energy in the worldwide electric power supply
increases the need for accurate solar radiation forecasts.
<br><br>
In this work, we present results of a very short term global horizontal
irradiance (GHI) forecast experiment based on hemispheric sky
images. A 2-month data set with images from one sky imager and high-resolution
GHI measurements from 99 pyranometers distributed over
10 km by 12 km is used for validation. We developed
a multi-step model and processed GHI forecasts up to 25 min with an
update interval of 15 s. A cloud type classification is used to
separate the time series into different cloud scenarios.
<br><br>
Overall, the sky-imager-based forecasts do not outperform the
reference persistence forecasts. Nevertheless, we find that analysis
and forecast performance depends strongly on the predominant cloud
conditions. Especially convective type clouds lead to high temporal
and spatial GHI variability. For cumulus cloud conditions, the
analysis error is found to be lower than that introduced by a single
pyranometer if it is used representatively for the whole area in
distances from the camera larger than 1–2 km. Moreover,
forecast skill is much higher for these conditions compared to
overcast or clear sky situations causing low GHI variability, which is
easier to predict by persistence. In order to generalize the
cloud-induced forecast error, we identify a variability threshold
indicating conditions with positive forecast skill. |
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| ISSN: | 1680-7316 1680-7324 |