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

Panagiotis Kosmopoulos, Dimitris Kouroutsidis, Kyriakoula Papachristopoulou, Panagiotis Ioannis Raptis, Akriti Masoom, Yves-Marie Saint-Drenan, Philippe Blanc, Charalampos Kontoes and Stelios Kazadzis
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Panagiotis Kosmopoulos: Institute for Environmental Research and Sustainable Development, National Observatory of Athens (IERSD/NOA), 15236 Athens, Greece
Dimitris Kouroutsidis: Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens (IAASARS/NOA), 15236 Athens, Greece
Kyriakoula Papachristopoulou: Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens (IAASARS/NOA), 15236 Athens, Greece
Panagiotis Ioannis Raptis: Institute for Environmental Research and Sustainable Development, National Observatory of Athens (IERSD/NOA), 15236 Athens, Greece
Akriti Masoom: Mechanical and Industrial Engineering Department, Indian Institute of Technology Roorkee, Roorkee 247667, India
Yves-Marie Saint-Drenan: O.I.E. Centre Observation, Impacts, Energy, MINES ParisTech, PSL Research University, 06904 Sophia Antipolis, France
Philippe Blanc: O.I.E. Centre Observation, Impacts, Energy, MINES ParisTech, PSL Research University, 06904 Sophia Antipolis, France
Charalampos Kontoes: Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens (IAASARS/NOA), 15236 Athens, Greece
Stelios Kazadzis: Physikalisch Meteorologisches Observatorium Davos, World Radiation Center (PMOD/WRC), CH-7260 Davos, Switzerland

Energies, 2020, vol. 13, issue 24, 1-22

Abstract: 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.

Keywords: solar power; short-term forecasting; cloud motion vector; cloud modification factor; cloud optical thickness (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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