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Open AccessArticle

Short-Term Forecasting of Large-Scale Clouds Impact on Downwelling Surface Solar Irradiation

1
Institute for Environmental Research and Sustainable Development, National Observatory of Athens (IERSD/NOA), 15236 Athens, Greece
2
Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens (IAASARS/NOA), 15236 Athens, Greece
3
Department of Geology and Geoenvironment, National and Kapodistrian University of Athens, 15784 Athens, Greece
4
Mechanical and Industrial Engineering Department, Indian Institute of Technology Roorkee, Roorkee 247667, India
5
O.I.E. Centre Observation, Impacts, Energy, MINES ParisTech, PSL Research University, 06904 Sophia Antipolis, France
6
Physikalisch Meteorologisches Observatorium Davos, World Radiation Center (PMOD/WRC), CH-7260 Davos, Switzerland
*
Author to whom correspondence should be addressed.
Energies 2020, 13(24), 6555; https://doi.org/10.3390/en13246555
Received: 17 November 2020 / Revised: 4 December 2020 / Accepted: 10 December 2020 / Published: 11 December 2020
This study focuses on the use of cloud motion vectors (CMV) and fast radiative transfer models (FRTM) in the prospect of forecasting downwelling surface solar irradiation (DSSI). Using near-real-time 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, we introduce a novel short-term forecasting system (3 h ahead) that is capable of calculating solar energy in large-scale (1.5 million-pixel area covering Europe and North Africa) and in high spatial (5 km over nadir) and temporal resolution (15 min intervals). For the operational implementation of such a big data computing architecture (20 million simulations in less than a minute), we exploit a synergy of high-performance computing and deterministic image processing technologies (dense optical flow estimation). The impact of clouds forecasting uncertainty on DSSI was quantified in terms of cloud modification factor (CMF), for all-sky and clear sky conditions, for more generalized results. The forecast accuracy was evaluated against the real COT and CMF images under different cloud movement patterns, and the correlation was found to range from 0.9 to 0.5 for 15 min and 3 h ahead, respectively. The CMV forecast variability revealed an overall DSSI uncertainty in the range 18–34% under consecutive alternations of cloud presence, highlighting the ability of the proposed system to follow the cloud movement in opposition to the baseline persistent forecasting, which considers the effects of topocentric sun path on DSSI but keeps the clouds in “fixed” positions, and which presented an overall uncertainty of 30–43%. The proposed system aims to support the distributed solar plant energy production management, as well as electricity handling entities and smart grid operations. View Full-Text
Keywords: solar power; short-term forecasting; cloud motion vector; cloud modification factor; cloud optical thickness solar power; short-term forecasting; cloud motion vector; cloud modification factor; cloud optical thickness
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MDPI and ACS Style

Kosmopoulos, P.; Kouroutsidis, D.; Papachristopoulou, K.; Raptis, P.I.; Masoom, A.; Saint-Drenan, Y.-M.; Blanc, P.; Kontoes, C.; Kazadzis, S. Short-Term Forecasting of Large-Scale Clouds Impact on Downwelling Surface Solar Irradiation. Energies 2020, 13, 6555.

AMA Style

Kosmopoulos P, Kouroutsidis D, Papachristopoulou K, Raptis PI, Masoom A, Saint-Drenan Y-M, Blanc P, Kontoes C, Kazadzis S. Short-Term Forecasting of Large-Scale Clouds Impact on Downwelling Surface Solar Irradiation. Energies. 2020; 13(24):6555.

Chicago/Turabian Style

Kosmopoulos, Panagiotis; Kouroutsidis, Dimitris; Papachristopoulou, Kyriakoula; Raptis, Panagiotis I.; Masoom, Akriti; Saint-Drenan, Yves-Marie; Blanc, Philippe; Kontoes, Charalampos; Kazadzis, Stelios. 2020. "Short-Term Forecasting of Large-Scale Clouds Impact on Downwelling Surface Solar Irradiation" Energies 13, no. 24: 6555.

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