Estimating solar irradiance using sky imagers

Abstract. Ground-based whole-sky cameras are now extensively used for the localized monitoring of clouds. They capture hemispherical images of the sky at regular intervals using a fish-eye lens. In this paper, we propose a framework for estimating solar irradiance from pictures taken by those imagers. Unlike pyranometers, such sky images contain information about cloud coverage and can be used to derive cloud movement. An accurate estimation of solar irradiance using solely those images is thus a first step towards the short-term forecasting of solar energy generation based on cloud movement. We derive and validate our model using pyranometers colocated with our whole-sky imagers. We achieve a better performance in estimating solar irradiance and in particular its short-term variations compared to other related methods using ground-based observations.

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Estimating Solar Irradiance Using Sky Imagers

10.5194/amt-2019-141 ◽

2019 ◽

Cited By ~ 1

Author(s):

Soumyabrata Dev ◽

Florian M. Savoy ◽

Yee Hui Lee ◽

Stefan Winkler

Keyword(s):

Solar Energy ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Solar Irradiance ◽

State Of The Art ◽

Accurate Estimation ◽

Mean Square ◽

Short Term ◽

Cloud Coverage

Abstract. Ground-based whole sky cameras are now-a-days extensively used for localized monitoring of the clouds. They capture hemispherical images of the sky at regular intervals using a fisheye lens. In this paper, we derive a model for estimating the solar irradiance using pictures taken by those imagers. Unlike pyranometers, these sky images contain information about the cloud coverage and can be used to derive cloud movement. An accurate estimation of the solar irradiance using solely those images is thus a first step towards short-term solar energy generation forecasting, as cloud movement can also be derived from them. We derive and validate our model using pyranometers co-located with our whole sky imagers. We achieve a root mean square error of 178 Watt/m2 between estimated and measured solar irradiance, outperforming state-of-the-art methods using other weather instruments. Our method shows a significant improvement in estimating strong short-term variations.

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Assessment of a newly developed short-term forecasting system (nextSENSE) of Downwelling Surface Solar Irradiance (DSSI) and validation with ground-based measurements

10.5194/egusphere-egu21-7218 ◽

2021 ◽

Author(s):

Kyriakoula Papachristopoulou ◽

Ilias Fountoulakis ◽

Panagiotis Kosmopoulos ◽

Dimitris Kouroutsidis ◽

Panagiotis I. Raptis ◽

...

Keyword(s):

Solar Energy ◽

Solar Irradiance ◽

Surface Radiation ◽

Solar Irradiation ◽

Short Term ◽

Large Spatial Scale ◽

Infrared Imager ◽

Flow Motion ◽

Forecasting System ◽

Short Term Forecasting

Monitoring and forecasting cloud coverage is crucial for nowcasting and forecasting of solar irradiance reaching the earth surface, and it&#8217;s a powerful tool for solar energy exploitation systems.In this study, we focused on the assessment of a newly developed short-term (up to 3h) forecasting system of Downwelling Surface Solar Irradiation (DSSI) in a large spatial scale (Europe and North Africa). This system forecasts the future cloud position by calculating Cloud Motion Vectors (CMV) using Cloud Optical Thickness (COT) data derived from multispectral images from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite and an optical flow motion estimation technique from the computer vision community. Using as input consecutive COT images, CMVs are calculated and cloud propagation is performed by applying them to the latest COT image. Using the predicted COT images, forecasted DSSI is calculated using Fast Radiative Transfer Models (FRTM) in high spatial (5 km over nadir) and temporal resolution (15 min time intervals intervals).A first evaluation of predicted COT has been conducted, by comparing the predicted cloud parameter of COT with real observed values derived by the MSG/SEVIRI. Here, the DSSI is validated against ground-based measurements from three Baseline Surface Radiation Network (BSRN) stations, for the year 2017. Also, a sensitivity analysis of the effect on DSSI for different cloud and aerosol conditions is performed, to ensure reliability under different sky and climatological conditions.The DSSI short-term forecasting system proposed, complements the existing short-term forecasting techniques and it is suitable for operational deployment of solar energy related systemsAcknowledgementsThis study was funded by the EuroGEO e-shape (grant agreement No 820852).

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Short-Term Forecasting of Surface Solar Irradiance Based on Meteosat-SEVIRI Data Using a Nighttime Cloud Index

Remote Sensing ◽

10.3390/rs70709070 ◽

2015 ◽

Vol 7 (7) ◽

pp. 9070-9090 ◽

Cited By ~ 13

Author(s):

Annette Hammer ◽

Jan Kühnert ◽

Kailash Weinreich ◽

Elke Lorenz

Keyword(s):

Solar Irradiance ◽

Short Term ◽

Short Term Forecasting

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Forecasting of solar irradiance and ramp events with all-sky imagers

10.5194/ems2021-75 ◽

2021 ◽

Author(s):

Stavros-Andreas Logothetis ◽

Vasileios Salamalikis ◽

Stefan Wilbert ◽

Jan Remund ◽

Luis Zarzalejo ◽

...

Keyword(s):

Solar Irradiance ◽

Large Scale ◽

Forecast Error ◽

Forecast Accuracy ◽

Short Term ◽

Event Prediction ◽

High Penetration ◽

Spanish Research ◽

Short Term Forecasting ◽

Solar Resource

Cloud cameras (all sky imagers/ASIs) can be used for short-term (next 20 min) forecasts of solar irradiance. For this reason, several experimental and operational solutions emerged in the last decade with different approaches in terms of instrument types and forecast algorithms. Moreover, few commercial and semi-prototype systems are already available or being investigated. So far, the uncertainty of the predictions cannot be fully compared, as previously published tests were carried out during different periods and at different locations. In this study, the results from a benchmark exercise are presented in order to qualify the current ASI-based short-term forecasting solutions and examine their accuracy. This first comparative measurement campaign carried out as part of the IEA PVPS Task 16 (https://iea-pvps.org/research-tasks/solar-resource-for-high-penetration-and-large-scale-applications/). A 3-month observation campaign (from August to December 2019) took place at Plataforma Solar de Almeria of the Spanish research center CIEMAT including five different ASI systems and a network of high-quality measurements of solar irradiance and other atmospheric parameters. Forecasted time-series of global horizontal irradiance are compared with ground-based measurements and two persistence models to identify strengths and weaknesses of each approach and define best practices of ASI-based forecasts. The statistical analysis is divided into seven cloud classes to interpret the different cloud type effect on ASIs forecast accuracy. For every cloud cluster, at least three ASIs outperform persistence models, in terms of forecast error, highlighting their performance capabilities. The feasibility of ASIs on ramp event detection is also investigated, applying different approaches of ramp event prediction. The revealed findings are promising in terms of overall performance of ASIs as well as their forecasting capabilities in ramp detection. &#160;

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