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Probabilistic solar nowcasting based on all-sky imagers

Nouri, Bijan and Wilbert, Stefan and Blum, Niklas and Fabel, Yann and Elke, Lorenz and Hammer, Annette and Thomas, Schmidt and Luis, Zarzalejo and Pitz-Paal, Robert (2023) Probabilistic solar nowcasting based on all-sky imagers. Solar Energy, 253, pp. 285-307. Elsevier. doi: 10.1016/j.solener.2023.01.060. ISSN 0038-092X.

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The interest in shortest-term solar irradiance forecasts (nowcasts) increases steadily with the increase share of distributed solar power generation. Such solar irradiance nowcasts are beneficial for different stakeholders, from generation to transmission and demand, in order to tackle challenges caused by the variability of solar irradiance. In space and time highly resolved nowcasts can be obtained by all sky imager (ASI) systems, which analyze the sky conditions from sky images. Deterministic nowcasts from ASI systems are subject to significant uncertainties. Reliable uncertainty information are very helpful for any application, in order to derive practical benefit from nowcasts. Therefore, such nowcasts should be probabilistic in nature, which provide probability distributions. Meaningful indicators for the uncertainties at hand are provided by prediction intervals for distinct confidence levels derived from the probability distributions. Thus, a real time capable nonparametric probabilistic quantile nowcasting method based on deterministic ASI nowcast is developed. The method takes irradiance variabilities as main predictor of nowcast uncertainties into account. A benchmark against three distinct baseline models is conducted over an extensive data set, using a variety of recently recommended scores. Overall average continuous ranked probability skill scores (Clear-Sky Dependent Climatology as baseline) for nowcasts up to 20 min ahead of 0.72 ± 0.08 (direct normal irradiance) and 0.62 ± 0.09 (global horizontal irradiance) are reached. For a better evaluation of the actual performance of the probabilistic nowcasts, a discretization of the validation data set into eight irradiance variability conditions is performed. All scores are determined for each of these distinct conditions.

Item URL in elib:https://elib.dlr.de/194153/
Document Type:Article
Title:Probabilistic solar nowcasting based on all-sky imagers
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Nouri, BijanUNSPECIFIEDhttps://orcid.org/0000-0002-9891-1974UNSPECIFIED
Wilbert, StefanUNSPECIFIEDhttps://orcid.org/0000-0003-3573-3004UNSPECIFIED
Blum, NiklasUNSPECIFIEDhttps://orcid.org/0000-0002-1541-7234UNSPECIFIED
Fabel, YannUNSPECIFIEDhttps://orcid.org/0000-0002-1892-5701UNSPECIFIED
Hammer, AnnetteUNSPECIFIEDhttps://orcid.org/0000-0002-5630-3620UNSPECIFIED
Thomas, SchmidtUNSPECIFIEDhttps://orcid.org/0000-0002-3205-2724UNSPECIFIED
Pitz-Paal, RobertUNSPECIFIEDhttps://orcid.org/0000-0002-3542-3391UNSPECIFIED
Date:March 2023
Journal or Publication Title:Solar Energy
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 285-307
Keywords:Probabilistic nowcasts Solar irradiance All sky imager Quantile forecast
HGF - Research field:Energy
HGF - Program:Materials and Technologies for the Energy Transition
HGF - Program Themes:High-Temperature Thermal Technologies
DLR - Research area:Energy
DLR - Program:E SW - Solar and Wind Energy
DLR - Research theme (Project):E - Condition Monitoring
Location: Köln-Porz
Institutes and Institutions:Institute of Solar Research > Qualification
Institute of Networked Energy Systems > Energy Systems Analysis, OL
Deposited By: Nouri, Bijan
Deposited On:03 Apr 2023 12:38
Last Modified:01 Jun 2023 10:54

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