Validation of spatially resolved all sky imager derived DNI nowcasts

Kuhn, Pascal Moritz; Wilbert, Stefan; Schüler, David; Prahl, Christoph; Haase, Thomas; Ramírez, Lourdes; Zarzalejo, Luis F.; Meyer, Angela; Vuilleumier, Laurent; Blanc, Philippe; Dubrana, Jean; Kazantzidis, Andreas; Schroedter-Homscheidt, Marion; Hirsch, Tobias; Pitz‐Paal, Robert

doi:10.1063/1.4984522

Cited by 19 publications

(10 citation statements)

References 9 publications

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“…Looking at Figure A, both the absolute and the relative RMSE values for GHI are displayed. As expected from the previous studies, higher lead times show higher deviations for RMSE, MAE, and standard deviation as the more distant future is more difficult to predict. Temporal aggregations reduce these deviations because of averaging effects.…”

Section: Validation Of Nowcasted Irradiance Mapssupporting

confidence: 85%

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

Kuhn¹,

Nouri²,

Wilbert³

et al. 2017

Progress in Photovoltaics

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International audienceBecause 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

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Section: Validation Of Nowcasted Irradiance Mapssupporting

confidence: 85%

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

Kuhn¹,

Nouri²,

Wilbert³

et al. 2017

Progress in Photovoltaics

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“…The shadow camera is a downward-facing camera placed on top of an 87 m high tower (CIEMAT CESA-I), which is part of a shadow camera system providing spatially resolved irradiance maps (Kuhn et al (2017a), Kuhn et al (2017b), Kuhn et al (2017c), Kuhn et al (2018a)). The shadow camera is used to measure reference cloud speeds, which are compared to the CSS.…”

Section: Introductionmentioning

confidence: 99%

Field validation and benchmarking of a cloud shadow speed sensor

Kuhn¹,

Wirtz²,

Wilbert³

et al. 2018

Solar Energy

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With ramp rate regulations for photovoltaic plants being discussed in many countries, the speed of clouds has gained signicant importance lately. Besides, measuring cloud velocities and directions is of interest for validations of numerical weather predictions and solar nowcasting systems. Recently, the Cloud Shadow Speed Sensor (CSS) was developed and validated in San Diego for low cumulus clouds. In this publication, the CSS is studied under dierent weather and cloud conditions in the desert of Tabernas in southern Spain. Furthermore, a novel shadow camera based low-cost, low-maintenance approach to determine cloud shadow motion vectors is presented and used as a reference to benchmark the CSS. In comparison, the absolute velocities derived from the CSS and the shadow camera on 59 days for ±5 min temporal medians show deviations of

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“…The irradiance maps produced by WobaS-4cam for lead times between 0 and 15 min are compared to the reference irradiance maps of the shadow camera system for field sizes between 5 m × 5 m (one pixel) and 4 km 2 . Unsurprisingly, longer lead times lead to larger deviations (Kuhn et al, 2017c). However, these deviations shrink significantly if spatial aggregation effects are considered.…”

Section: Studying Spatial Aggregation Effects On Nowcasted Irradiancementioning

confidence: 99%

Applications of a shadow camera system for energy meteorology

Kuhn¹,

Wilbert²,

Prahl³

et al. 2018

Adv. Sci. Res.

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Abstract. Downward-facing shadow cameras might play a major role in future energy meteorology. Shadow cameras directly image shadows on the ground from an elevated position. They are used to validate other systems (e.g. all-sky imager based nowcasting systems, cloud speed sensors or satellite forecasts) and can potentially provide short term forecasts for solar power plants. Such forecasts are needed for electricity grids with high penetrations of renewable energy and can help to optimize plant operations. In this publication, two key applications of shadow cameras are briefly presented.

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Validation of spatially resolved all sky imager derived DNI nowcasts

Cited by 19 publications

References 9 publications

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

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

Field validation and benchmarking of a cloud shadow speed sensor

Applications of a shadow camera system for energy meteorology

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