Applying self-supervised learning for semantic cloud segmentation of all-sky images

This work presents a new approach to exploit unlabeled image data from ground-based sky observations to train neural networks. We show that our model can detect cloud classes within images more accurately than models trained with conventional methods using small, labeled datasets only. Novel machine learning techniques as applied in this work enable training with much larger datasets, leading to improved accuracy in cloud detection and less need for manual image labeling

Validation of an all-sky imager based nowcasting system for industrial PV plants

Because of the cloud-induced variability of the solar resource, the growing contributions of photovoltaic plants to the overall power generation challenges the stability of electricity grids. To avoid blackouts, administrations started to define maximum negative ramp rates. Storages can be used to reduce the occurring ramps. Their required capacity, durability, and costs can be optimized by nowcasting systems. Nowcasting systems use the input of upward-facing cameras to predict future irradiances. Previously, many nowcasting systems were developed and validated. However, these validations did not consider aggregation effects, which are present in industrial-sized power plants. In this paper, we present the validation of nowcasted global horizontal irradiance (GHI) and direct normal irradiance maps derived from an example system consisting of 4 all-sky cameras (“WobaS-4cam”). The WobaS-4cam system is operational at 2 solar energy research centers and at a commercial 50-MW solar power plant. Besides its validation on 30 days, the working principle is briefly explained. The forecasting deviations are investigated with a focus on temporal and spatial aggregation effects. The validation found that spatial and temporal aggregations significantly improve forecast accuracies: Spatial aggregation reduces the relative root mean square error (GHI) from 30.9% (considering field sizes of 25 m2) to 23.5% (considering a field size of 4 km2) on a day with variable conditions for 1 minute averages and a lead time of 15 minutes. Over 30 days of validation, a relative root mean square error (GHI) of 20.4% for the next 15 minutes is observed at pixel basis (25 m2). Although the deviations of nowcasting systems strongly depend on the validation period and the specific weather conditions, the WobaS-4cam system is considered to be at least state of the art

Development and Benchmarking of Solar Nowcasting Systems

The cloud induced variability of the solar resource is an operational challenge for both photovoltaic and concentrating solar plants. This and their growing contributions to overall power generation challenge the stability of electricity grids. Storage and grid development can be used to address this issue, whose deployments are optimized by forecasts. For several days ahead, solar forecasts are provided by Numerical Weather Prediction models with typical resolutions of 16-50 km and hours to days. Weather satellites provide solar irradiance predictions with kilometric spatial resolutions and typical temporal resolutions between 5 min and 15 min. This leaves a gap for local, short-term and high resolution solar forecasting systems. High resolution forecasts for the next minutes ahead are notably provided by all-sky imager (ASI) based nowcasting systems. Such systems consist of at least one camera taking hemispherical images of the sky. A large amount of hardware and software configurations to derive such short-term forecasts is proposed in literature. However, different validation periods, locations and approaches as well as the lack of comprehensive benchmarks make direct comparisons difficult. Therefore, the optimal configuration of ASI based nowcasting systems is unclear. To address this question, a modular framework to achieve high resolution spatially resolved solar forecasts for irradiance maps is developed. Within this framework, different hardware and software configurations are tested, highlighting the need for comprehensive benchmarks for each main step involved in the processing: cloud segmentation, cloud motion vector assessment, cloud height estimation. Four cloud segmentation approaches are benchmarked. Furthermore, a novel tool to validate cloud motion vector measurements is developed and applied, ruling out certain setups. Comparing five different cloud height measurement approaches found a setup based on two ASIs to be the most promising. The optimal distance between the cameras of such a two ASI system is investigated both with an in-field study and modeling. Besides benchmarking individual sub-tasks of nowcasting systems, a framework to evaluate nowcasted irradiance maps is applied to example configurations. Special focus is put on temporal and spatial aggregation effects. In order to investigate spatial Aggregation effects, spatially resolved reference irradiance maps provided by a developed shadow camera system are compared to nowcasted irradiance maps. Aggregation effects are found to have a strong effect on deviations. With the implemented frameworks and conducted benchmarks, the optimal system among the considered configurations to nowcast irradiance maps is determined to be a two camera setup using a clear sky library based cloud segmentation as well as novel differential approaches for cloud height and motion vector estimations. Further options for improvements are identified to include in-depth studies of cloud dynamics, combinations of ASIs with other sensors such as satellites, optimized cloud segmentation algorithms and the development of systems dedicated to solar ramp forecasts

Development and benchmarking of solar nowcasting systems

The cloud induced variability of the solar resource is an operational challenge for both photovoltaic and concentrating solar plants. This and their growing contributions to overall power generation challenge the stability of electricity grids. Storage and grid development can be used to address this issue, whose deployments are optimized by forecasts. For several days ahead, solar forecasts are provided by Numerical Weather Prediction models with typical resolutions of 16-50 km and hours to days. Weather satellites provide solar irradiance predictions with kilometric spatial resolutions and typical temporal resolutions between 5 min and 15 min. This leaves a gap for local, short-term and high resolution solar forecasting systems. High resolution forecasts for the next minutes ahead are notably provided by all-sky imager (ASI) based nowcasting systems. Such systems consist of at least one camera taking hemispherical images of the sky. A large amount of hardware and software configurations to derive such short-term forecasts is proposed in literature. However, different validation periods, locations and approaches as well as the lack of comprehensive benchmarks make direct comparisons difficult. Therefore, the optimal configuration of ASI based nowcasting systems is unclear. To address this question, a modular framework to achieve high resolution spatially resolved solar forecasts for irradiance maps is developed. Within this framework, different hardware and software configurations are tested, highlighting the need for comprehensive benchmarks for each main step involved in the processing: cloud segmentation, cloud motion vector assessment, cloud height estimation. Four cloud segmentation approaches are benchmarked. Furthermore, a novel tool to validate cloud motion vector measurements is developed and applied, ruling out certain setups. Comparing five different cloud height measurement approaches found a setup based on two ASIs to be the most promising. The optimal distance between the cameras of such a two ASI system is investigated both with an in-field study and modeling. Besides benchmarking individual sub-tasks of nowcasting systems, a framework to evaluate nowcasted irradiance maps is applied to example configurations. Special focus is put on temporal and spatial aggregation effects. In order to investigate spatial aggregation effects, spatially resolved reference irradiance maps provided by a developed shadow camera system are compared to nowcasted irradiance maps. Aggregation effects are found to have a strong effect on deviations. With the implemented frameworks and conducted benchmarks, the optimal system among the considered configurations to nowcast irradiance maps is determined to be a two camera setup using a clear sky library based cloud segmentation as well as novel differential approaches for cloud height and motion vector estimations. Further options for improvements are identified to include in-depth studies of cloud dynamics, combinations of ASIs with other sensors such as satellites, optimized cloud segmentation algorithms and the development of systems dedicated to solar ramp forecasts

Benchmarking of six cloud segmentation algorithms for ground-based all-sky imagers

The detection and segmentation of clouds in images taken by ground based cameras is of utmost importance for a large number of applications including all-sky imager based nowcasting systems which optimize solar power plant operation, calculation of the global irradiance, estimation of the cloud base height and support of optical satellite downlink operations. Many approaches to segment clouds in camera images are published. However, comparisons of different approaches are not frequently conducted. Here, we address this question by benchmarking six different cloud segmentation algorithms on images taken by an off-the-shelf surveillance camera. The six different algorithms include (1) a color-channel threshold-based algorithm, (2) a Clear Sky Library (CSL) based approach, (3) a region growing algorithm, (4) the Hybrid thresholding algorithm (HYTA), and a (5) novel, HYTA-based development named HYTA+. Furthermore, (6) a deep convolutional neural network (FCN) is adapted via transfer learning to this problem. The segmentation results of algorithms (1) to (5) are compared to 829 manually segmented reference images. The segmentation algorithms are benchmarked on a test dataset which is divided into 16 meteorological categories. These categories cover different Linke turbidity values, solar positions and cloud cover situations. Results show that three out of the six presented segmentation methods (CSL, HYTA+ and FCN) achieve overall accuracy values above 90%. These approaches outperform the other methods and correctly segment images with a higher consistency. Fixed threshold based methods, as the multicolor criterion, HYTA or the region growing algorithm fail under certain meteorological conditions. The FCN based segmentation (6) is tested on 160 images where it delivers the best overall pixel-by-pixel accuracy of 97.0%

Energiemeteorologisches Wolkenkameranetzwerk für die hochaufgelöste Kurzfristprognose der solaren Einstrahlung

Im Zuge der fortschreitenden Integration von stark fluktuierenden Energiequellen wie insbesondere der Photovoltaik entsteht ein wachsender Bedarf an hochwertigen Erzeugungsdaten, wobei zunehmend Informationen zur zeitlich und räumlich hochaufgelösten Struktur und zur Variabilität der Erzeugung benötigt werden. In diesem Zusammenhang entsteht derzeit in der nordwestdeutschen Weser-Ems-Region ein neuartiges Messnetz bestehend aus 34 Wolkenkameras sowie mehreren Einstrahlungs- und Wolkenhöhemessungen. Das in einem Gebiet der Grö0e von ca. 60x80 km verteilte Netzwerk dient der Entwicklung und Anwendung einer regionalen Kurzfirstprognose der solaren Einstrahlung in einer sehr hohen räumlichen und zeitlichen Auflösung auf Basis von lokalen Messungen, Wolkenkameras und Satellitenbildern. Das Netzwerk bietet erstmals die Möglichkeit, kurzfristig präzise Prognosen der lokalen Bewölkung und der daraus resultierenden bodennahen Einstrahlung zu generieren. Das Netzwerk aus Kameras ermöglicht die großräumige Abdeckung von größeren Städten und Regionen. Bei einer räumlichen Auflösung von wenigen Metern und einer zeitlichen Auflösung von weniger als einer Minute können sowohl kleinskalige Bewölkungsstrukturen aufgelöst als auch kurzfristige Wolkenentwicklungen erkannt und vorhergesagt werden. Insbesondere Betreiber von Photovoltaikanlagen, Stromhändler und Stromnetzbetreiber können von diesen Vorhersagedaten profitieren. Der Beitrag stellt das Netzwerk, die verwendete Methodik und mögliche Anwendungsfälle für die Grundlagenforschung und Akteure der Energiewirtschaft vor. Erste Messergebnisse aus dem Netzwerk zeigen exemplarisch das Potenital dier neuartigen Infrastruktur