scholarly journals Data-driven short-term forecasting of solar irradiance profile

2017 ◽  
Vol 143 ◽  
pp. 572-578 ◽  
Author(s):  
Poh Soon Loh ◽  
Jialing Vivien Chua ◽  
Aik Chong Tan ◽  
Cheng Im Khaw
Keyword(s):  
Solar Irradiance ◽  
Data Driven ◽  
Short Term ◽  
Short Term Forecasting

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>

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>

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 Solar Irradiance Forecasting Using a Data-Driven Algorithm and Contextual Optimisation

2021 ◽  
Vol 12 (1) ◽  
pp. 134
Author(s):  
Paula Bendiek ◽  
Ahmad Taha ◽  
Qammer H. Abbasi ◽  
Basel Barakat
Keyword(s):  
Solar Irradiance ◽  
Contextual Information ◽  
Management Strategies ◽  
Energy Transition ◽  
Data Driven ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Short Term ◽  
Forecast Horizon

Solar forecasting plays a key part in the renewable energy transition. Major challenges, related to load balancing and grid stability, emerge when a high percentage of energy is provided by renewables. These can be tackled by new energy management strategies guided by power forecasts. This paper presents a data-driven and contextual optimisation forecasting (DCF) algorithm for solar irradiance that was comprehensively validated using short- and long-term predictions, in three US cities: Denver, Boston, and Seattle. Moreover, step-by-step implementation guidelines to follow and reproduce the results were proposed. Initially, a comparative study of two machine learning (ML) algorithms, the support vector machine (SVM) and Facebook Prophet (FBP) for solar prediction was conducted. The short-term SVM outperformed the FBP model for the 1- and 2- hour prediction, achieving a coefficient of determination (R2) of 91.2% in Boston. However, FBP displayed sustained performance for increasing the forecast horizon and yielded better results for 3-hour and long-term forecasts. The algorithms were optimised by further contextual model adjustments which resulted in substantially improved performance. Thus, DCF utilised SVM for short-term and FBP for long-term predictions and optimised their performance using contextual information. DCF achieved consistent performance for the three cities and for long- and short-term predictions, with an average R2 of 85%.

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