FN Archimer Export Format
PT J
TI Nowcasting solar irradiance using an analog method and geostationary satellite images
BT 
AF AYET, Alex
   TANDEO, P.
AS 1:1,2;2:3;
FF 1:PDG-ODE-LOPS-SIAM;2:;
C1 Elum Energy, Paris, France.
   IFREMER, CNRS, IRD, UBO,LOPS,UMR 6523,IUEM, Plouzane, France.
   IMT Atlantique, LabSTICC, UBL, F-29238 Brest, France.
C2 ELUM ENERGY, FRANCE
   IFREMER, FRANCE
   IMT ATLANTIQUE, FRANCE
SI BREST
SE PDG-ODE-LOPS-SIAM
UM LOPS
IN WOS Ifremer jusqu'en 2018
   copubli-france
IF 4.674
TC 31
UR https://archimer.ifremer.fr/doc/00431/54229/56000.pdf
LA English
DT Article
DE ;Satellite-derived irradiance;Short term forecasting;Analog method;Geostationary satellite;PV
AB Accurate forecasting of Global Horizontal Irradiance (GHI) is essential for the integration of the solar resource in an electrical grid. We present a novel data-driven method aimed at delivering up to 6 h hourly probabilistic forecasts of GHI on top of a localized solar energy source. The method does not require calibration to adapt to regional differences in cloud dynamics, and uses only one type of data, covering Europe and Africa. It is thus suited for applications that require a GHI forecast for solar energy sources at different locations with few ground measurements. Cloud dynamics are emulated using an analog method based on 5 years of hourly images of geostationary satellite-derived irradiance, without using any numerical prediction model. This database contains both the images to be compared to the current atmospheric observation and their successors at one or more hours of interval. The physics of the system is emulated statistically, and no numerical prediction model is used. The method is tested on one year of data and five locations in Europe with different climatic conditions. It is compared to persistence (keeping the last observation frozen), ensemble persistence (generating a probabilistic forecast using the last observations) and an adaptive first order vector autoregressive model. As an application, the model is downscaled using ground measurements. In both cases, the analog method outperforms the classical statistical approaches. Results demonstrate the skill of the method in emulating cloud dynamics, and its potential to be coupled with a forecasting algorithm using ground measurements for operational applications. 
PY 2018
PD APR
SO Solar Energy
SN 0038-092X
PU Pergamon-elsevier Science Ltd
VL 164
UT 000430774700031
BP 301
EP 315
DI 10.1016/j.solener.2018.02.068
ID 54229
ER

EF