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 Comparison Between Measurements and WRF Numerical Simulation of Global Solar Irradiation in Romania

2013 ◽  
Vol 57 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Dragos Isvoranu ◽  
Viorel Badescu
Keyword(s):  
Numerical Simulation ◽  
Preliminary Analysis ◽  
Measured Data ◽  
Solar Irradiation ◽  
Short Term ◽  
Data Accessibility ◽  
Power Stations ◽  
Photovoltaic Power ◽  
Global Solar Irradiation ◽  
Global Irradiation

Abstract The paper presents a comparative analysis between the surface global irradiation measured for Romania and the predicted irradiation obtained by numerical simulation. The measured data came from the Romanian National meteorological Administration. Based on a preliminary analysis that took into account several criteria among which, performance, cost, popularity and meteorological and satellite data accessibility we concluded that a combination GFS-WRF(NMM) or GFS-WRF(ARW) is most suitable for short term global solar irradiation forecasting in order to assess the performance of the photovoltaic power stations (Badescu and Dumitrescu, 2012, [1], Martin et al., 2011, [2]).

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 IRRADIAÇÃO SOLAR GLOBAL PARA CIDADE DE MAPUTO - MOÇABIQUE: EVOLUÇÃO TEMPORAL DAS MEDIDAS E MODELAGEM ESTATÍSTICA

2019 ◽  
Vol 34 (01) ◽  
pp. 82-93
Author(s):  
Domingos Mário Zeca Fernando ◽  
Marcus Vinicius Contes Calca ◽  
Matheus Rodrigues Raniero ◽  
Alexandre Dal Pai
Keyword(s):  
Solar Radiation ◽  
Statistical Modeling ◽  
Solar Irradiance ◽  
Temporal Evolution ◽  
Solar Irradiation ◽  
Global Media ◽  
Global Solar Irradiation ◽  
Maputo City ◽  
The City ◽  
E Mail

IRRADIAÇÃO SOLAR GLOBAL PARA CIDADE DE MAPUTO - MOÇABIQUE: EVOLUÇÃO TEMPORAL DAS MEDIDAS E MODELAGEM ESTATÍSTICA   DOMINGOS MÁRIO ZECA FERNANDO¹; MARCUS VINÍCIUS CONTES CALÇA²; MATHEUS RODRIGUES RANIERO³ E ALEXANDRE DAL PAI4   ¹Departamento de Ciências Naturais e Matemática, Universidade Púnguè,  Bairro Heróis Moçambicanos, C. Postal: 323, Cidade de Chimoio, Moçambique, e-mail: [email protected]. 2Departamento de bioprocessos e biotecnologia, Universidade Estadual Paulista, Av. Universitária, nº 3780 - Altos do Paraíso, Botucatu - SP, 18610-034, Brasil, e-mail: [email protected]. 3Departamento de bioprocessos e biotecnologia, Universidade Estadual Paulista, Av. Universitária, nº 3780 - Altos do Paraíso, Botucatu - SP, 18610-034, Brasil, e-mail: [email protected]. 4Departamento de bioprocessos e biotecnologia, Universidade Estadual Paulista, Av. Universitária, nº 3780 - Altos do Paraíso, Botucatu - SP, 18610-034, Brasil, e-mail: [email protected].   RESUMO: A equação de Angstrom continua sendo a relação mais usada para se estimar a irradiação solar global média diária a partir das horas de brilho solar, em localidades onde a medição não seja contínua ou os equipamentos não estejam disponíveis. O objetivo deste trabalho foi de apresentar a evolução das medidas de irradiação solar e das horas de brilho solar e determinar os coeficientes de Angstrom para estimativa da irradiação solar global na cidade de Maputo. A média da irradiação solar global na cidade de Maputo foi de 17,96 MJ/m² e as horas de brilho solar foram de 7,8 horas. Os coeficientes “a” e “b” do modelo anual para estimativa da irradiação solar global na cidade de Maputo são 0,23 e 0,49 respetivamente.   Palavras-chaves: brilho solar, estimativa irradiação solar global, modelo de angstrom.   GLOBAL SOLAR IRRADIATION FOR MAPUTO CITY - MOZAMBIQUE: TEMPORAL EVOLUTION OF MEASUREMENTS AND STATISTICAL MODELING   ABSTRACT: The Angstrom equation continues to be the most used relation to estimate the daily average global solar irradiation from sunshine, in places where measurement is not continuous or equipment is not available. The aim of this work was to present the evolution of solar radiation and sunshine measurements and to determine the Angstrom coefficients to estimate global solar irradiation in the city of Maputo. The average of the global solar irradiance in the city of Maputo was 17.96 MJ/m² and sunshine average was 7.8 hours. The coefficients "a" and "b" of the annual model for estimation of global solar irradiation in the city of Maputo were 0.23 and 0.49, respectively.   Keywords: sunshine, estimation of global solar irradiation, Angstrom model.

scholarly journals The Ultra-Short-Term Forecasting of Global Horizonal Irradiance Based on Total Sky Images

2020 ◽  
Vol 12 (21) ◽  
pp. 3671
Author(s):  
Junxia Jiang ◽  
Qingquan Lv ◽  
Xiaoqing Gao
Keyword(s):  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Solar Irradiation ◽  
Short Term ◽  
Clear Sky ◽  
Forecasting Models ◽  
Forecasting Performance ◽  
Sky Imager ◽  
Short Term Forecasting ◽  
Sky Conditions

Solar photovoltaics (PV) has advanced at an unprecedented rate and the global cumulative installed PV capacity is growing exponentially. However, the ability to forecast PV power remains a key technical challenge due to the variability and uncertainty of solar irradiance resulting from the changes of clouds. Ground-based remote sensing with high temporal and spatial resolution may have potential for solar irradiation forecasting, especially under cloudy conditions. To this end, we established two ultra-short-term forecasting models of global horizonal irradiance (GHI) using Ternary Linear Regression (TLR) and Back Propagation Neural Network (BPN), respectively, based on the observation of a ground-based sky imager (TSI-880, Total Sky Imager) and a radiometer at a PV plant in Dunhuang, China. Sky images taken every 1 min (minute) were processed to determine the distribution of clouds with different optical depths (thick, thin) for generating a two-dimensional cloud map. To obtain the forecasted cloud map, the Particle Image Velocity (PIV) method was applied to the two consecutive images and the cloud map was advected to the future. Further, different types of cloud fraction combined with clear sky index derived from the GHI of clear sky conditions were used as the inputs of the two forecasting models. Limited validation on 4 partly cloudy days showed that the average relative root mean square error (rRMSE) of the 4 days ranged from 5% to 36% based on the TLR model and ranged from 12% to 32% based on the BPN model. The forecasting performance of the BPN model was better than the TLR model and the forecasting errors increased with the increase in lead time.

Effect of short-term use of oral contraceptive pills on intraocular pressure

2020 ◽  
Vol 35 (3) ◽  
Author(s):  
Tayyaba Gul Malik ◽  
Hina Nadeem ◽  
Eiman Ayesha ◽  
Rabail Alam
Keyword(s):  
Oral Contraceptive ◽  
Childbearing Age ◽  
Short Term ◽  
Oral Contraceptive Pills ◽  
Contraceptive Pills ◽  
Time Period ◽  
Significant Difference ◽  
Group A ◽  
Group B ◽  
Short Time

Objective: To study the effect of short-term use of oral contraceptive pills on intra-ocular pressures of women of childbearing age.   Methods: It was a comparative observational study, conducted at Arif memorial teaching hospital and Allied hospital Faisalabad for a period of six months. Hundred female subjects were divided into two groups of 50 each. Group A, included females, who had been taking oral contraceptive pills (OCP) for more than 6 months and less than 36 months. Group B, included 50 age-matched controls, who had never used OCP. Ophthalmic and systemic history was taken. Careful Slit lamp examination was performed and intraocular pressures (IOP) were measured using Goldman Applanation tonometer. Fundus examination was done to rule out any posterior segment disease. After collection of data, we analyzed and compared the intra ocular pressures between the two groups by using ANOVA in SPSS version 21.   Results: Average duration of using OCP was 14.9 months. There was no significant difference of Cup to Disc ratios between the two groups (p= 0.109). However, significant difference was noted between the IOP of OCP group and controls. (p=0.000). Conclusion: OCP significantly increase IOP even when used for short time period.

scholarly journals Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3030
Author(s):  
Simon Liebermann ◽  
Jung-Sup Um ◽  
YoungSeok Hwang ◽  
Stephan Schlüter
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Renewable Energy Sources ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Weather Data ◽  
Short Term ◽  
Climate Conditions ◽  
Lstm Network ◽  
The Time Domain

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations it shows that combining an LSTM network with a CNN yields the best performance. However, regarding the chosen GoF measures, differences to the alternative approaches are fairly small. The hybrid model’s advantage lies not in the improved GoF but in its versatility: contrary to an LSTM or a CNN, it produces good results under all tested weather conditions.

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