Articles | Volume 16, issue 5
Research article
15 Mar 2016
Research article |  | 15 Mar 2016

Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts

Thomas Schmidt, John Kalisch, Elke Lorenz, and Detlev Heinemann

Abstract. 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.

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.

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.

Short summary
We performed an irradiance forecast experiment based on analysis of hemispheric sky images and evaluated results on a large data set of 99 pyranometers distributed over 10 × 12 km. We developed a surface irradiance retrieval from cloud information derived from the images. Very high resolution forecasts were processed up to 25 min. A main finding is that forecast skill is enhanced in complex cloud conditions leading to high variability in surface irradiance.
Final-revised paper