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Evaluation of the Solar Energy Nowcasting System (SENSE) during a 12-Months Intensive Measurement Campaign in Athens, Greece
Evaluation of the Solar Energy Nowcasting System (SENSE) during a 12-Months Intensive Measurement Campaign in Athens, Greece
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Evaluation of the Solar Energy Nowcasting System (SENSE) during a 12-Months Intensive Measurement Campaign in Athens, Greece
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Evaluation of the Solar Energy Nowcasting System (SENSE) during a 12-Months Intensive Measurement Campaign in Athens, Greece
Evaluation of the Solar Energy Nowcasting System (SENSE) during a 12-Months Intensive Measurement Campaign in Athens, Greece

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Evaluation of the Solar Energy Nowcasting System (SENSE) during a 12-Months Intensive Measurement Campaign in Athens, Greece
Evaluation of the Solar Energy Nowcasting System (SENSE) during a 12-Months Intensive Measurement Campaign in Athens, Greece
Journal Article

Evaluation of the Solar Energy Nowcasting System (SENSE) during a 12-Months Intensive Measurement Campaign in Athens, Greece

2023
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Overview
Energy nowcasting is a valuable asset in managing energy loads and having real-time information on solar irradiation availability. In this study, we evaluate the spectrally integrated outputs of the SENSE system for solar irradiance nowcasting for the period of the ASPIRE (atmospheric parameters affecting spectral solar irradiance and solar energy) campaign (December 2020–December 2021) held in Athens, Greece. For the needs of the campaign, several ground-based instruments were operating, including two pyranometers, a pyrheliometer, a cloud camera, a CIMEL sunphotometer, and a precision spectral radiometer (PSR). Global horizontal irradiance (GHI) estimations were more accurate than direct normal irradiance (DNI). SENSE estimations are provided every 15 min, but when comparing bigger time intervals (hours-days), the statistics improved. A dedicated assessment of the SENSE’s inputs is performed in respect to ground-based retrievals, considering cloud conditions (from a sky imager), AOD, and precipitable water vapor from AERONET. The factor that established the larger errors was the visibility of the solar disc, which cannot be defined by the available sources of model inputs. Additionally, there were discrepancies between the satellite estimation of the clouds and the ground picture, which caused deviations in results. AOD differences affected more the DNI.