scholarly journals An application of the ECMWF Ensemble Prediction System for short-term solar power forecasting

Solar Energy ◽  
2016 ◽  
Vol 133 ◽  
pp. 437-450 ◽  
Author(s):  
Simone Sperati ◽  
Stefano Alessandrini ◽  
Luca Delle Monache
Keyword(s):  
Solar Power ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Short Term ◽  
Ensemble Prediction System ◽  
Solar Power Forecasting ◽  
Power Forecasting

Probabilistic precipitation nowcast for flash flooding purposes across Australia: verification of a new version of Short-Term Ensemble Prediction System (STEPS)

2021 ◽  
Author(s):  
Carlos Velasco-Forero ◽  
Jayaram Pudashine ◽  
Mark Curtis ◽  
Alan Seed
Keyword(s):  
Lead Time ◽  
Precipitation Forecast ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Prediction System ◽  
Short Term ◽  
Radar Rainfall ◽  
Flash Flooding ◽  
Ensemble Prediction System ◽  
Study Results

<div> <p>Short-term precipitation forecast plays a vital role for minimizing the adverse effects of heavy precipitation events such as flash flooding.  Radar rainfall nowcasting techniques based on statistical extrapolations are used to overcome current limitations of precipitation forecasts from numerical weather models, as they provide high spatial and temporal resolutions forecasts within minutes of the observation time. Among various algorithms, the Short-Term Ensemble Prediction System (STEPS) provides rainfall fields nowcasts in a probabilistic sense by accounting the uncertainty in the precipitation forecasts by means of ensembles, with spatial and temporal characteristic very similar to those in the observed radar rainfall fields. The Australian Bureau of Meteorology uses STEPS to generate ensembles of forecast rainfall ensembles in real-time from its extensive weather radar network. </p> </div><div> <p>In this study, results of a large probabilistic verification exercise to a new version of STEPS (hereafter named STEPS-3) are reported. An extensive dataset of more than 47000 individual 5-minute radar rainfall fields (the equivalent of more than 163 days of rain) from ten weather radars across Australia (covering tropical to mid-latitude regions) were used to generate (and verify) 96-member rainfall ensembles nowcasts with up to a 90-minute lead time. STEPS-3 was found to be more than 15-times faster in delivering results compared with previous version of STEPS and an open-source algorithm called pySTEPS. Interestingly, significant variations were observed in the quality of predictions and verification results from one radar to other, from one event to other, depending on the characteristics and location of the radar, nature of the rainfall event, accumulation threshold and lead time. For example, CRPS and RMSE of ensembles of 5-min rainfall forecasts for radars located in mid-latitude regions are better (lower) than those ones from radars located in tropical areas for all lead-times. Also, rainfall fields from S-band radars seem to produce rainfall forecasts able to successfully identify extreme rainfall events for lead times up to 10 minutes longer than those produced using C-band radar datasets for the same rain rate thresholds. Some details of the new STEPS-3 version, case studies and examples of the verification results will be presented. </p> </div>

Short-term solar power forecasting using satellite images

2021 ◽  
Vol 10 (2) ◽  
pp. 125
Author(s):  
A. Shobana Devi ◽  
G. Maragatham ◽  
K. Boopathi ◽  
M.R. Prabu
Keyword(s):  
Satellite Images ◽  
Solar Power ◽  
Short Term ◽  
Solar Power Forecasting ◽  
Power Forecasting

scholarly journals New Developments in Ensemble-based Probabilistic Forecasting of Solar Radiation: The WRF-Solar Ensemble Prediction System

2021 ◽  
Author(s):  
Manajit Sengupta ◽  
Pedro Jimenez ◽  
Jaemo Yang ◽  
Ju-Hye Kim ◽  
Yu Xie
Keyword(s):  
Sensitivity Analysis ◽  
Solar Radiation ◽  
Solar Irradiance ◽  
Solar Power ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Stochastic Perturbations ◽  
Radiation Data ◽  
Solar Generation

<p>The demand for increased accuracy in predicting solar power has grown considerably over recent years due to a rapid growth in grid-tied solar generation both utility scale and distributed. To increase confidence in forecasting solar power there is a need to provide reliable probabilistic solar radiation information that also minimizes error and uncertainty. Funded by the U.S. Department of Energy, the Weather Research and Forecasting (WRF)-Solar ensemble prediction system (WRF-Solar EPS) has been recently developed by a collaboration between the National Renewable Energy Laboratory and the National Center for Atmospheric Research. The WRF-Solar EPS is now ready to be disseminated to support the integration of solar generation resources and improve accuracy of day-ahead and intraday probabilistic solar forecasts. The first stage of our framework in developing WRF-Solar EPS required a specially designed method using a tangent linear (TL) sensitivity analysis to efficiently investigate uncertainties of WRF-Solar variables in forecasting clouds and solar irradiance. For the second stage, we applied a methodology to introduce stochastic perturbations in 14 key variables ascertained through the TL sensitivity analysis in generating ensemble members. A user-friendly interface is provided in WRF-Solar EPS, in which the parameters of stochastic perturbations can be controlled by configuration files. Lastly, we implemented an analog technique as an ensemble post-processing method to improve the performance of ensemble solar irradiance forecasts. This presentation will summarize the work performed in the past 3 years to understand the interactions between cloud physics, land surface, boundary layer and radiative transfer models through the development of a probabilistic cloud optimized day-ahead forecasting system based on WRF-Solar. For evaluation of forecasts, we adapt and use satellite-derived solar radiation data, e.g., the National Solar Radiation Data Base (NSRDB) as well as ground-measured observations. A comprehensive analysis to assess gridded model outputs over the Contiguous U.S is performed. The importance of evaluation of the WRF-Solar model with the NSRDB lies in the fact that the knowledge of the cloud-caused uncertainties in predicting solar irradiance over a wide range of regions provides model developers a detailed understanding of model strength and weaknesses in predicting clouds. Overall, we will present the detailed research steps that resulted in the development of the WRF-Solar EPS. We will also present a detailed validation demonstrating the improvements provided by this model. Moreover, we will also introduce the user’s guide for WRF-Solar EPS and future extension of this research.</p>

scholarly journals Online short-term solar power forecasting

Solar Energy ◽  
2009 ◽  
Vol 83 (10) ◽  
pp. 1772-1783 ◽  
Author(s):  
Peder Bacher ◽  
Henrik Madsen ◽  
Henrik Aalborg Nielsen
Keyword(s):  
Solar Power ◽  
Short Term ◽  
Solar Power Forecasting ◽  
Power Forecasting
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