Assessment of a newly developed short-term forecasting system (nextSENSE) of Downwelling Surface Solar Irradiance (DSSI) and validation with ground-based measurements

2021 ◽  
Author(s):  
Kyriakoula Papachristopoulou ◽  
Ilias Fountoulakis ◽  
Panagiotis Kosmopoulos ◽  
Dimitris Kouroutsidis ◽  
Panagiotis I. Raptis ◽  
...  
Keyword(s):  
Solar Energy ◽  
Solar Irradiance ◽  
Surface Radiation ◽  
Solar Irradiation ◽  
Short Term ◽  
Large Spatial Scale ◽  
Infrared Imager ◽  
Flow Motion ◽  
Forecasting System ◽  
Short Term Forecasting

<p>Monitoring and forecasting cloud coverage is crucial for nowcasting and forecasting of solar irradiance reaching the earth surface, and it’s a powerful tool for solar energy exploitation systems.</p><p>In this study, we focused on the assessment of a newly developed short-term (up to 3h) forecasting system of Downwelling Surface Solar Irradiation (DSSI) in a large spatial scale (Europe and North Africa). This system forecasts the future cloud position by calculating Cloud Motion Vectors (CMV) using 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 and an optical flow motion estimation technique from the computer vision community. Using as input consecutive COT images, CMVs are calculated and cloud propagation is performed by applying them to the latest COT image. Using the predicted COT images, forecasted DSSI is calculated using Fast Radiative Transfer Models (FRTM) in high spatial (5 km over nadir) and temporal resolution (15 min time intervals intervals).</p><p>A first evaluation of predicted COT has been conducted, by comparing the predicted cloud parameter of COT with real observed values derived by the MSG/SEVIRI. Here, the DSSI is validated against ground-based measurements from three Baseline Surface Radiation Network (BSRN) stations, for the year 2017. Also, a sensitivity analysis of the effect on DSSI for different cloud and aerosol conditions is performed, to ensure reliability under different sky and climatological conditions.</p><p>The DSSI short-term forecasting system proposed, complements the existing short-term forecasting techniques and it is suitable for operational deployment of solar energy related systems</p><p>Acknowledgements</p><p>This study was funded by the EuroGEO e-shape (grant agreement No 820852).</p>

scholarly journals Estimating solar irradiance using sky imagers

2019 ◽  
Vol 12 (10) ◽  
pp. 5417-5429 ◽  
Author(s):  
Soumyabrata Dev ◽  
Florian M. Savoy ◽  
Yee Hui Lee ◽  
Stefan Winkler
Keyword(s):  
Solar Energy ◽  
Solar Irradiance ◽  
Energy Generation ◽  
Accurate Estimation ◽  
Eye Lens ◽  
Short Term ◽  
Cloud Coverage ◽  
Short Term Forecasting ◽  
Fish Eye

Abstract. Ground-based whole-sky cameras are now extensively used for the localized monitoring of clouds. They capture hemispherical images of the sky at regular intervals using a fish-eye lens. In this paper, we propose a framework for estimating solar irradiance from pictures taken by those imagers. Unlike pyranometers, such sky images contain information about cloud coverage and can be used to derive cloud movement. An accurate estimation of solar irradiance using solely those images is thus a first step towards the short-term forecasting of solar energy generation based on cloud movement. We derive and validate our model using pyranometers colocated with our whole-sky imagers. We achieve a better performance in estimating solar irradiance and in particular its short-term variations compared to other related methods using ground-based observations.

scholarly journals Short-Term Forecasting of Surface Solar Irradiance Based on Meteosat-SEVIRI Data Using a Nighttime Cloud Index

2015 ◽  
Vol 7 (7) ◽  
pp. 9070-9090 ◽  
Author(s):  
Annette Hammer ◽  
Jan Kühnert ◽  
Kailash Weinreich ◽  
Elke Lorenz
Keyword(s):  
Solar Irradiance ◽  
Short Term ◽  
Short Term Forecasting

scholarly journals Forecasting of solar irradiance and ramp events with all-sky imagers

2021 ◽  
Author(s):  
Stavros-Andreas Logothetis ◽  
Vasileios Salamalikis ◽  
Stefan Wilbert ◽  
Jan Remund ◽  
Luis Zarzalejo ◽  
...  
Keyword(s):  
Solar Irradiance ◽  
Large Scale ◽  
Forecast Error ◽  
Forecast Accuracy ◽  
Short Term ◽  
Event Prediction ◽  
High Penetration ◽  
Spanish Research ◽  
Short Term Forecasting ◽  
Solar Resource

<p>Cloud cameras (all sky imagers/ASIs) can be used for short-term (next 20 min) forecasts of solar irradiance. For this reason, several experimental and operational solutions emerged in the last decade with different approaches in terms of instrument types and forecast algorithms. Moreover, few commercial and semi-prototype systems are already available or being investigated. So far, the uncertainty of the predictions cannot be fully compared, as previously published tests were carried out during different periods and at different locations. In this study, the results from a benchmark exercise are presented in order to qualify the current ASI-based short-term forecasting solutions and examine their accuracy. This first comparative measurement campaign carried out as part of the IEA PVPS Task 16 (https://iea-pvps.org/research-tasks/solar-resource-for-high-penetration-and-large-scale-applications/). A 3-month observation campaign (from August to December 2019) took place at Plataforma Solar de Almeria of the Spanish research center CIEMAT including five different ASI systems and a network of high-quality measurements of solar irradiance and other atmospheric parameters. Forecasted time-series of global horizontal irradiance are compared with ground-based measurements and two persistence models to identify strengths and weaknesses of each approach and define best practices of ASI-based forecasts. The statistical analysis is divided into seven cloud classes to interpret the different cloud type effect on ASIs forecast accuracy. For every cloud cluster, at least three ASIs outperform persistence models, in terms of forecast error, highlighting their performance capabilities. The feasibility of ASIs on ramp event detection is also investigated, applying different approaches of ramp event prediction. The revealed findings are promising in terms of overall performance of ASIs as well as their forecasting capabilities in ramp detection.  </p>

Short-term forecasting of solar irradiance

2019 ◽  
Vol 143 ◽  
pp. 985-994 ◽  
Author(s):  
Marius Paulescu ◽  
Eugenia Paulescu
Keyword(s):  
Solar Irradiance ◽  
Short Term ◽  
Short Term Forecasting

scholarly journals Preliminary Wrf-Arw Model Analysis of Global Solar Irradiation Forecasting

2014 ◽  
Vol 10 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Dragos Isvoranu ◽  
Viorel Badescu
Keyword(s):  
Solar Irradiance ◽  
Solar Irradiation ◽  
Short Term ◽  
Simulation Data ◽  
Power Stations ◽  
Global Solar Irradiation ◽  
Time Period ◽  
Forecasting Performance ◽  
Arw Model ◽  
Best Fit

Abstract The purpose of this research is focused on the evaluation of short term global solar irradiation forecasting performance in order to assess the outcome of photovoltaic power stations. The paper presents a comparative analysis between the predicted irradiation obtained by numerical simulation and measurements. The simulation data is obtained from WRF-ARW model (Weather Research Forecasting-Advanced Research WRF), whose initial and boundary conditions are provided by the global forecasting model GFS. Taking into account the complexity of options for the physics models provided with WRF, we embarked upon a parametric analysis of the simulated solar irradiance. This complex task provides a better insight among the coupling of various physics options and enables us to find the best fit with the measured data for a specified site and time period. The present preliminary analysis shows that the accuracy of the computed global solar irradiance can be improved by choosing the appropriate built-in physics models. A combination of physics models providing the best results has been identified.

scholarly journals Solar Irradiance Assessment and Forecasting in Tropical Climates using Satellite Remote Sensing and Physical Modelling

2021 ◽  
Author(s):  
Akriti Masoom ◽  
Panagiotis Kosmopolous ◽  
Ankit Bansal
Keyword(s):  
Solar Energy ◽  
Optical Depth ◽  
Solar Irradiance ◽  
Distribution System ◽  
Wrf Model ◽  
Physical Modelling ◽  
Final Analysis ◽  
Surface Radiation ◽  
Atmospheric Composition ◽  
Energy Potential

<p>Poor resolution of solar irradiance ground data demonstrates the necessity and provides an opportunity for satellite data-based solar irradiance modeling. The study is focused on India due to its tropical climate that provides varied as well as extreme conditions for solar energy research. For solar energy monitoring in near real-time, the Indian Solar Irradiance Operational System (INSIOS) was developed using operational cloud and aerosol data from INSAT-3D and Copernicus Atmosphere Monitoring Service (CAMS)-Monitoring Atmospheric Composition Climate, respectively. It had high accuracy under clear-sky conditions for global horizontal irradiance (GHI) and direct normal irradiance (DNI) that were evaluated for a year at four Baseline Surface Radiation Network (BSRN) stations located in urban regions. The presented methodology could effectively support the penetration of photovoltaic installation as estimations were reliable during high solar energy potential conditions with median BSRN and INSIOS difference varying from 93 to 49 W/m<sup>2</sup> for GHI.</p><p>Further, an operational day-ahead solar irradiance forecasting system (WRF-CAMS) is presented that ingests CAMS aerosol optical depth (AOD) into the WRF model to better quantify the aerosol impact on solar energy long-term forecasts, and validation was done against ground-based measurements from BSRN stations. The analysis was carried out for forecast horizons varying from 24 h to 36 h for different seasons, varying solar zenith angles, and different cloud cover classifications based on calculated clearness index. The correlation coefficient was improved from 0.93 to 0.95 for GHI and 0.75 to 0.82 for DNI after the ingestion of CAMS AOD as compared to WRF default aerosol scheme. The annual root mean square error was observed to vary from 10 to 130 W/m<sup>2</sup> and 50 to 190 W/m<sup>2</sup> for GHI and DNI, respectively. This system is hoped to provide more accurate forecasts for better solar plant energy planning and improve day-to-day electricity exchange market supporting solar energy producers and distribution system operators.</p><p>In the final analysis, INSIOS and WRF-CAMS models were used for forecasting dust impact on solar irradiance during an extreme dust event using Aeronet measurements, satellite observations (MODIS and CALIPSO), and ModIs Dust AeroSol (MIDAS) dust database. WRF-CAMS model was used to examine dust impact on solar irradiance for a high-intensity dust storm with AOD and dust optical depth values reaching up to 2. The observed average decrease in GHI and DNI due to the dust plume was 76 W/m<sup>2</sup> and 275 W/m<sup>2</sup>, respectively, and a maximum reduction of 100 W/m<sup>2</sup> (10%) and 400 W/m<sup>2</sup> (40%), respectively. The proposed methodology can support solar energy producers, for optimum energy production forecasting, management, and maintenance as well as transmission and distribution system operators, as dust events of this extent significantly reduce solar irradiance and affect energy exploitation capacity due to solar aerosol-related extinction.</p>

Sign in / Sign up

Export Citation Format

Share Document