Original paper
A new sky imager based global irradiance forecasting model with analyses of cirrus situations
Dittmann, Anna; Holland, Nicolas; Lorenz, Elke
Meteorologische Zeitschrift Vol. 30 No. 2 (2021), p. 101 - 113
35 references
published: Apr 22, 2021
published online: Jul 14, 2020
manuscript accepted: May 9, 2020
manuscript revision received: Apr 20, 2020
manuscript revision requested: Feb 17, 2020
manuscript received: Dec 20, 2019
Open Access (paper may be downloaded free of charge)
Abstract
High resolution irradiance forecasts based on sky imagers are valuable for applications that require short term decisions based on ramps of solar irradiance. Here, we present our sky imager based forecasting algorithm, using images of a low cost surveillance camera. Model development and evaluation is done separately for the different steps in sky imager forecasting, starting with cloud detection, followed by estimation and extrapolation of cloud movement, and finally deriving irradiance forecasts from the predicted cloud images. We distinguish between clear and cloudy conditions and especially evaluate the effect of cirrus situations on the different forecasting steps. To create binary cloud masks, we adapted a pixel value based cloud algorithm using a set of manually classified pixels. In an independent validation dataset 90.3 % of the pixels are classified correctly. For the circumsolar region, where cloud decision is known to be especially challenging and crucial for the forecasting of the direct component of the solar radiation, we introduce a correction procedure using real-time irradiance measurements and object recognition methods. Applying this method we can significantly improve the cloud detection in the circumsolar region and increase the forecast skill of the cloud decision forecast. The development of the irradiance algorithm is a special focus of this paper. Real-time irradiance measurements and cloud decision information are used as input to our irradiance model. The algorithm is developed using cloud decision information derived from measurements instead of sky imager cloud decision forecasts to exclude the influence of errors in cloud decision and cloud motion methods for model development. Afterwards, the irradiance algorithm is applied to sky imager based cloud decision forecasts. Even though we start with binary cloud information, the distribution of the clear sky index from our forecasts is in very good agreement with the distribution of the measurements. In a validation dataset of 46 days, we receive a positive forecast skill for all forecast horizons larger than 1 min. We also apply our forecast chain to a dataset of two month from an independent measurement station resulting in a comparable forecasting performance.
Keywords
sky imager • short-term solar forecasting • irradiance model • cloud detection • circumsolar region