Benchmarking three low-cost, low-maintenance cloud height measurement systems and ECMWF cloud heights against a ceilometer

Kuhn, Pascal Moritz; Wirtz, Michael; Killius, Niels; Wilbert, Stefan; Bosch, J.L.; Hanrieder, Natalie; Nouri, Bijan; Kleissl, Jan; Ramírez, Lourdes; Schroedter-Homscheidt, Marion; Heinemann, Detlev; Kazantzidis, Andreas; Blanc, Philippe; Pitz‐Paal, Robert

doi:10.1016/j.solener.2018.02.050

Cited by 21 publications

(22 citation statements)

References 29 publications

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“…If the CSS is used as a part of an all-sky imager based nowcasting system or utilized to derive cloud heights, an inexpensive camera could be added, providing a complete system. A CSS and a shadow camera based system, which derives cloud heights, is presented and validated against a ceilometer on the same 59 days in another publication (Kuhn et al, 2018b).…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

“…12. The deviations stem mostly from optically thin clouds and clouds at large altitudes (see Kuhn et al (2018b)). If such clouds trigger CSS measurements at all, the accuracy is poor.…”

Section: The Shadow Camera Referencementioning

confidence: 99%

Field validation and benchmarking of a cloud shadow speed sensor

Kuhn¹,

Wirtz²,

Wilbert³

et al. 2018

Solar Energy

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“…As an example for the validation of a subtask, the validation of a cloud detection approach is discussed in this section. Validations of other subtask, for instance, the derivation of cloud heights and cloud speeds, are presented elsewhere.…”

Section: Validation Of Cloud Segmentationmentioning

confidence: 99%

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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“…This provides the system with added value in terms of functionality and gives extra data to incorporate in the next steps of the image processing and forecasting pipeline. A recent paper comparing the use of different instruments to measure the CBH concluded that using a pair of inexpensive cameras was the most cost-effective alternative in comparison with other methods such as a ceilometer or LIDAR [48]. In fact, cloud base height is quite important to estimate the position of the shadows if a ray tracing approach is taken and it can also be included as a feature if Machine Learning methods are preferred, as it correlates with the position of the clouds in the image and the recorded irradiance or PV production.…”

Section: Skyimager In the Canary Islandsmentioning

confidence: 99%

Validation of All-Sky Imager Technology and Solar Irradiance Forecasting at Three Locations: NREL, San Antonio, Texas, and the Canary Islands, Spain

et al. 2019

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Increasing photovoltaic (PV) generation in the world’s power grid necessitates accurate solar irradiance forecasts to ensure grid stability and reliability. The University of Texas at San Antonio (UTSA) SkyImager was designed as a low cost, edge computing, all-sky imager that provides intra-hour irradiance forecasts. The SkyImager utilizes a single board computer and high-resolution camera with a fisheye lens housed in an all-weather enclosure. General Purpose IO pins allow external sensors to be connected, a unique aspect is the use of only open source software. Code for the SkyImager is written in Python and calls libraries such as OpenCV, Scikit-Learn, SQLite, and Mosquito. The SkyImager was first deployed in 2015 at the National Renewable Energy Laboratory (NREL) as part of the DOE INTEGRATE project. This effort aggregated renewable resources and loads into microgrids which were then controlled by an Energy Management System using the OpenFMB Reference Architecture. In 2016 a second SkyImager was installed at the CPS Energy microgrid at Joint Base San Antonio. As part of a collaborative effort between CPS Energy, UT San Antonio, ENDESA, and Universidad de La Laguna, two SkyImagers have also been deployed in the Canary Islands that utilize stereoscopic images to determine cloud heights. Deployments at three geographically diverse locations not only provided large amounts of image data, but also operational experience under very different climatic conditions. This resulted in improvements/additions to the original design: weatherproofing techniques, environmental sensors, maintenance schedules, optimal deployment locations, OpenFMB protocols, and offloading data to the cloud. Originally, optical flow followed by ray-tracing was used to predict cumulus cloud shadows. The latter problem is ill-posed and was replaced by a machine learning strategy with impressive results. R2 values for the multi-layer perceptron of 0.95 for 5 moderately cloudy days and 1.00 for 5 clear sky days validate using images to determine irradiance. The SkyImager in a distributed environment with cloud-computing will be an integral part of the command and control for today’s SmartGrid and Internet of Things.

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Benchmarking three low-cost, low-maintenance cloud height measurement systems and ECMWF cloud heights against a ceilometer

Cited by 21 publications

References 29 publications

Field validation and benchmarking of a cloud shadow speed sensor

Field validation and benchmarking of a cloud shadow speed sensor

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

Validation of All-Sky Imager Technology and Solar Irradiance Forecasting at Three Locations: NREL, San Antonio, Texas, and the Canary Islands, Spain

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