Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts

Schmidt, Thomas; Kalisch, John; Lorenz, Elke; Heinemann, Detlev

doi:10.5194/acp-16-3399-2016

Cited by 68 publications

(57 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the same time, the limited durations of the campaigns result in a data set that extends from mid-spring to mid-autumn, and may not be representative of other times of the year. Schmidt et al (2016) use data from the Jülich campaign for a performance evaluation of sky-imager-based solar irradiance forecasts, and Madhavan et al (2016) present a more detailed discussion of the campaign and the instrumentation. To the best of the authors' knowledge, no other PV-related studies based on comparably dense and high-frequency irradiance sensor networks have been published to date.…”

Section: Measurement Campaignsmentioning

confidence: 99%

Local short-term variability in solar irradiance

2016

Self Cite

View full text Add to dashboard Cite

Abstract. Characterizing spatiotemporal irradiance variability is important for the successful grid integration of increasing numbers of photovoltaic (PV) power systems. Using 1 Hz data recorded by as many as 99 pyranometers during the HD(CP) 2 Observational Prototype Experiment (HOPE), we analyze field variability of clear-sky index k * (i.e., irradiance normalized to clear-sky conditions) and sub-minute k * increments (i.e., changes over specified intervals of time) for distances between tens of meters and about 10 km. By means of a simple classification scheme based on k * statistics, we identify overcast, clear, and mixed sky conditions, and demonstrate that the last of these is the most potentially problematic in terms of short-term PV power fluctuations. Under mixed conditions, the probability of relatively strong k * increments of ±0.5 is approximately twice as high compared to increment statistics computed without conditioning by sky type. Additionally, spatial autocorrelation structures of k * increment fields differ considerably between sky types. While the profiles for overcast and clear skies mostly resemble the predictions of a simple model published by Hoff and Perez (2012), this is not the case for mixed conditions. As a proxy for the smoothing effects of distributed PV, we finally show that spatial averaging mitigates variability in k * less effectively than variability in k * increments, for a spatial sensor density of 2 km −2 .

show abstract

Section: Measurement Campaignsmentioning

confidence: 99%

Local short-term variability in solar irradiance

2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…Schmidt et al (2016) made use of time series of hemispheric sky images to predict the surface irradiance by means of mapping the cloud position, which in turn is translated into shadow maps at the surface. The temporal evolution of such shadow maps is calculated from cloud motion vectors that were calculated from subsequent sky images.…”

Section: Near-surface Wind Field and Energy Budgetmentioning

confidence: 99%

The HD(CP)<sup>2</sup> Observational Prototype Experiment (HOPE) – an overview

et al. 2017

Self Cite

View full text Add to dashboard Cite

Abstract. The HD(CP) 2 Observational Prototype Experiment (HOPE) was performed as a major 2-month field experiment in Jülich, Germany, in April and May 2013, followed by a smaller campaign in Melpitz, Germany, in September 2013. HOPE has been designed to provide an observational dataset for a critical evaluation of the new German community atmospheric icosahedral non-hydrostatic (ICON) model at the scale of the model simulations and further to provide information on land-surface-atmospheric boundary layer exchange, cloud and precipitation processes, as well as sub-grid variability and microphysical properties that are subject to parameterizations. HOPE focuses on the onset of clouds and precipitation in the convective atmospheric boundary layer. This paper summarizes the instrument set-ups, the intensive observation periods, and example results from both campaigns.HOPE-Jülich instrumentation included a radio sounding station, 4 Doppler lidars, 4 Raman lidars (3 of them providePublished by Copernicus Publications on behalf of the European Geosciences Union. temperature, 3 of them water vapour, and all of them particle backscatter data), 1 water vapour differential absorption lidar, 3 cloud radars, 5 microwave radiometers, 3 rain radars, 6 sky imagers, 99 pyranometers, and 5 sun photometers operated at different sites, some of them in synergy. The HOPEMelpitz campaign combined ground-based remote sensing of aerosols and clouds with helicopter-and balloon-based in situ observations in the atmospheric column and at the surface.HOPE provided an unprecedented collection of atmospheric dynamical, thermodynamical, and micro-and macrophysical properties of aerosols, clouds, and precipitation with high spatial and temporal resolution within a cube of approximately 10 × 10 × 10 km 3 . HOPE data will significantly contribute to our understanding of boundary layer dynamics and the formation of clouds and precipitation. The datasets have been made available through a dedicated data portal.First applications of HOPE data for model evaluation have shown a general agreement between observed and modelled boundary layer height, turbulence characteristics, and cloud coverage, but they also point to significant differences that deserve further investigations from both the observational and the modelling perspective.

show abstract

“…The method showed an accuracy of 90% for five classes of sky conditions. Regarding classification machine learning algorithms, the literature contains proposals ranging from artificial neural networks (Kliangsuwan & Heednacram, 2015;Lee et al, 1990;Singh & Glennen, 2005), to k-nearest neighbor (KNN) (Cheng & Yu, 2015;Heinle et al, 2010;Kazantzidis et al, 2012;Wacker et al, 2015) and support vector machines (SVM) (Schmidt et al, 2015;Taravat et al, 2015;Zhen et al, 2015). ANNs are a commonly machine learning technique used in cloud classification.…”

Section: Introductionmentioning

confidence: 99%

Automatic Cloud‐Type Classification Based On the Combined Use of a Sky Camera and a Ceilometer

et al. 2017

View full text Add to dashboard Cite

A methodology, aimed to be fully operational, for automatic cloud classification based on the synergetic use of a sky camera and a ceilometer is presented. The random forest machine learning algorithm was used to train the classifier with 19 input features: 12 extracted from the sky camera images and 7 from the ceilometer. The method was developed and tested based on a set of 717 images collected at the radiometric stations of the Univ. of Jaén (Spain). Up to nine different types of clouds (plus clear sky) were considered (clear sky, cumulus, stratocumulus, nimbostratus, altocumulus, altostratus, stratus, cirrocumulus, cirrostratus, and cirrus) plus an additional category multicloud, aiming to account for the frequent cases in which the sky is covered by several cloud types. A total of eight experiments was conducted by (1) excluding/including the ceilometer information, (2) including/excluding the multicloud category, and (3) using six or nine different cloud types, aside from the clear‐sky and multicloud category. The method provided accuracies ranging from 45% to 78%, being highly dependent on the use of the ceilometer information. This information showed to be particularly relevant for accurately classifying “cumuliform” clouds and to account for the multicloud category. In this regard, the camera information alone was found to be not suitable to deal with this category. Finally, while the use of the ceilometer provided an overall superior performance, some limitations were found, mainly related to the classification of clouds with similar cloud base height and geometric thickness.

show abstract

Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts

Cited by 68 publications

References 34 publications

Local short-term variability in solar irradiance

Local short-term variability in solar irradiance

The HD(CP)<sup>2</sup> Observational Prototype Experiment (HOPE) – an overview

Automatic Cloud‐Type Classification Based On the Combined Use of a Sky Camera and a Ceilometer

Contact Info

Product

Resources

About

Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts

Cited by 68 publications

References 34 publications

Local short-term variability in solar irradiance

Local short-term variability in solar irradiance

The HD(CP)&lt;sup&gt;2&lt;/sup&gt; Observational Prototype Experiment (HOPE) – an overview

Automatic Cloud‐Type Classification Based On the Combined Use of a Sky Camera and a Ceilometer

Contact Info

Product

Resources

About

The HD(CP)<sup>2</sup> Observational Prototype Experiment (HOPE) – an overview