The Experts below are selected from a list of 2136 Experts worldwide ranked by ideXlab platform
Yehia Eissa - One of the best experts on this subject based on the ideXlab platform.
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validation of the surface downwelling solar Irradiance estimates of the helioclim 3 database in egypt
Remote Sensing, 2015Co-Authors: Yehia Eissa, Philippe Blanc, Hosni Ghedira, Mohamed Korany, Youva Aoun, Mohamed Boraiy, Magdy Abdel Wahab, S C Alfaro, Mossad Elmetwally, Katja HungershoeferAbstract:HelioClim-3 (HC3) is a database providing time series of the surface downwelling solar Irradiance that are computed from images of the Meteosat satellites. This paper presents the validation results of the hourly Global Horizontal Irradiance (GHI) and direct normal Irradiance (DNI), i.e., beam Irradiance at normal incidence, of versions four and five of HC3 at seven Egyptian sites. The validation is performed for all-sky conditions, as well as cloud-free conditions. Both versions of HC3 provide similar performances whatever the conditions. Another comparison is made with the estimates provided by the McClear database that is restricted to cloud-free conditions. All databases capture well the temporal variability of the GHI in all conditions, McClear being superior for cloud-free cases. In cloud-free conditions for the GHI, the relative root mean square error (RMSE) are fairly similar, ranging from 6% to 15%; both HC3 databases exhibit a smaller bias than McClear. McClear offers an overall better performance for the cloud-free DNI estimates. For all-sky conditions, the relative RMSE for GHI ranges from 10% to 22%, except one station, while, for the DNI, the results are not so good for the two stations with DNI measurements.
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Validating surface downwelling solar Irradiances estimated by the McClear model under cloud-free skies in the United Arab Emirates
Solar Energy, 2015Co-Authors: Yehia Eissa, Philippe Blanc, Hosni Ghedira, Armel Oumbe, Lucien Wald, Hélène Bru, Saima Munawwar, Dominique GoffeAbstract:McClear, a fast model based on a radiative transfer solver, exploits the atmospheric properties provided by the EU-funded MACC project (Monitoring Atmospheric Composition and Climate) to estimate the surface downwelling solar Irradiances for cloud-free instances. This article presents the first validation of the McClear model for the specific climate of the United Arab Emirates where skies are frequently cloud-free but turbid. McClear accurately estimates the Global Horizontal Irradiance measured every 10 min at seven sites. The bias ranges from -9 W m-2 (-1% of the mean observed Irradiance) to +35 W m-2 (+6%). The root mean square error (RMSE) ranges from 22 W m-2 (4%) to 47 W m-2 (8%) and the coefficient of determination ranges from 0.980 to 0.990. Estimates of the direct Irradiance at normal incidence exhibit an underestimation that is attributed to the overestimation of the aerosol optical depth in the MACC data set and not accounting for the circumsolar radiation in McClear. The corresponding bias ranges from -57 W m-2 (-8%) to +6 W m-2 (+1%). The RMSE ranges from 62 W m-2 (9%) to 87 W m-2 (13%) and the coefficient of determination ranges from 0.830 to 0.863. When compared to two other models in the literature, McClear is better able to capture the temporal variability of the direct Irradiance at normal incidence. The validation results remain comparable for the Global Horizontal Irradiance.
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artificial neural network based model for retrieval of the direct normal diffuse Horizontal and Global Horizontal Irradiances using seviri images
Solar Energy, 2013Co-Authors: Yehia Eissa, Hosni Ghedira, Prashanth Reddy Marpu, Imen Gherboudj, Taha B M J Ouarda, Matteo ChiesaAbstract:Abstract A statistical model for the prediction of the solar Irradiance components, utilizing six thermal channels of the SEVIRI instrument (onboard Meteosat Second Generation satellite), is presented. Additional inputs to the model include the solar zenith angle, solar time, day number and eccentricity correction. Treating the cloud-free and cloudy observations separately, the model employs two trained artificial neural network ensembles, one for estimating the direct normal Irradiance and the other for estimating the diffuse Horizontal Irradiance. The Global Horizontal Irradiance is then computed from the model’s outputs. The model has been trained using reference data from three ground measurement stations for the full year of 2010 and tested over two independent stations for the full year of 2009. Over the two independent stations for all sky conditions, the relative root mean square errors for the direct, diffuse and Global components are 26.1%, 25.6% and 12.4%, respectively, while the relative mean bias errors are −6%, +3.6% and −2.9%, respectively. The temporal and spatial variations of the direct, diffuse and Global components are also presented for three days exhibiting different sky conditions in the year 2009.
Philippe Blanc - One of the best experts on this subject based on the ideXlab platform.
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A New Approach for Satellite-Based Probabilistic Solar Forecasting with Cloud Motion Vectors
'MDPI AG', 2021Co-Authors: Thomas Carrière, Rodrigo Amaro E Silva, Fuqiang Zhuang, Yves-marie Saint-drenan, Philippe BlancAbstract:Probabilistic solar forecasting is an issue of growing relevance for the integration of photovoltaic (PV) energy. However, for short-term applications, estimating the forecast uncertainty is challenging and usually delegated to statistical models. To address this limitation, the present work proposes an approach which combines physical and statistical foundations and leverages on satellite-derived clear-sky index (kc) and cloud motion vectors (CMV), both traditionally used for deterministic forecasting. The forecast uncertainty is estimated by using the CMV in a different way than the one generally used by standard CMV-based forecasting approach and by implementing an ensemble approach based on a Gaussian noise-adding step to both the kc and the CMV estimations. Using 15-min average ground-measured Global Horizontal Irradiance (GHI) data for two locations in France as reference, the proposed model shows to largely surpass the baseline probabilistic forecast Complete History Persistence Ensemble (CH-PeEn), reducing the Continuous Ranked Probability Score (CRPS) between 37% and 62%, depending on the forecast horizon. Results also show that this is mainly driven by improving the model’s sharpness, which was measured using the Prediction Interval Normalized Average Width (PINAW) metric
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validation of the surface downwelling solar Irradiance estimates of the helioclim 3 database in egypt
Remote Sensing, 2015Co-Authors: Yehia Eissa, Philippe Blanc, Hosni Ghedira, Mohamed Korany, Youva Aoun, Mohamed Boraiy, Magdy Abdel Wahab, S C Alfaro, Mossad Elmetwally, Katja HungershoeferAbstract:HelioClim-3 (HC3) is a database providing time series of the surface downwelling solar Irradiance that are computed from images of the Meteosat satellites. This paper presents the validation results of the hourly Global Horizontal Irradiance (GHI) and direct normal Irradiance (DNI), i.e., beam Irradiance at normal incidence, of versions four and five of HC3 at seven Egyptian sites. The validation is performed for all-sky conditions, as well as cloud-free conditions. Both versions of HC3 provide similar performances whatever the conditions. Another comparison is made with the estimates provided by the McClear database that is restricted to cloud-free conditions. All databases capture well the temporal variability of the GHI in all conditions, McClear being superior for cloud-free cases. In cloud-free conditions for the GHI, the relative root mean square error (RMSE) are fairly similar, ranging from 6% to 15%; both HC3 databases exhibit a smaller bias than McClear. McClear offers an overall better performance for the cloud-free DNI estimates. For all-sky conditions, the relative RMSE for GHI ranges from 10% to 22%, except one station, while, for the DNI, the results are not so good for the two stations with DNI measurements.
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Validating surface downwelling solar Irradiances estimated by the McClear model under cloud-free skies in the United Arab Emirates
Solar Energy, 2015Co-Authors: Yehia Eissa, Philippe Blanc, Hosni Ghedira, Armel Oumbe, Lucien Wald, Hélène Bru, Saima Munawwar, Dominique GoffeAbstract:McClear, a fast model based on a radiative transfer solver, exploits the atmospheric properties provided by the EU-funded MACC project (Monitoring Atmospheric Composition and Climate) to estimate the surface downwelling solar Irradiances for cloud-free instances. This article presents the first validation of the McClear model for the specific climate of the United Arab Emirates where skies are frequently cloud-free but turbid. McClear accurately estimates the Global Horizontal Irradiance measured every 10 min at seven sites. The bias ranges from -9 W m-2 (-1% of the mean observed Irradiance) to +35 W m-2 (+6%). The root mean square error (RMSE) ranges from 22 W m-2 (4%) to 47 W m-2 (8%) and the coefficient of determination ranges from 0.980 to 0.990. Estimates of the direct Irradiance at normal incidence exhibit an underestimation that is attributed to the overestimation of the aerosol optical depth in the MACC data set and not accounting for the circumsolar radiation in McClear. The corresponding bias ranges from -57 W m-2 (-8%) to +6 W m-2 (+1%). The RMSE ranges from 62 W m-2 (9%) to 87 W m-2 (13%) and the coefficient of determination ranges from 0.830 to 0.863. When compared to two other models in the literature, McClear is better able to capture the temporal variability of the direct Irradiance at normal incidence. The validation results remain comparable for the Global Horizontal Irradiance.
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Very short term forecasting of the Global Horizontal Irradiance using a spatio-temporal autoregressive model
Renewable Energy, 2014Co-Authors: Romain Dambreville, Philippe Blanc, Jocelyn Chanussot, Didier BoldoAbstract:The integration of massive solar energy supply in the existing grids requires an accurate forecast of the solar resources to manage the energetic balance. In this context, we propose a new approach to forecast the Global Horizontal Irradiance at ground level from satellite images and ground based measurements. The training of spatio-temporal multidimensional autoregressive models with HelioClim-3 data along with 15-min averaged GHI times series is tested with respect to a ground based station from the BSRN network. Forecast horizons from 15 min to 1 h provided very promising results validated on a one year ground-based pyranometric data set. The performances have been compared to another similar method from the literature by means of relative metrics. The proposed approach paves the way of the use of satellite-based surface solar Irradiance (SSI) estimation as an SSI map nowcasting method that enables to capture spatio-temporal correlation for the improvement of a local SSI forecast.
Hosni Ghedira - One of the best experts on this subject based on the ideXlab platform.
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validation of the surface downwelling solar Irradiance estimates of the helioclim 3 database in egypt
Remote Sensing, 2015Co-Authors: Yehia Eissa, Philippe Blanc, Hosni Ghedira, Mohamed Korany, Youva Aoun, Mohamed Boraiy, Magdy Abdel Wahab, S C Alfaro, Mossad Elmetwally, Katja HungershoeferAbstract:HelioClim-3 (HC3) is a database providing time series of the surface downwelling solar Irradiance that are computed from images of the Meteosat satellites. This paper presents the validation results of the hourly Global Horizontal Irradiance (GHI) and direct normal Irradiance (DNI), i.e., beam Irradiance at normal incidence, of versions four and five of HC3 at seven Egyptian sites. The validation is performed for all-sky conditions, as well as cloud-free conditions. Both versions of HC3 provide similar performances whatever the conditions. Another comparison is made with the estimates provided by the McClear database that is restricted to cloud-free conditions. All databases capture well the temporal variability of the GHI in all conditions, McClear being superior for cloud-free cases. In cloud-free conditions for the GHI, the relative root mean square error (RMSE) are fairly similar, ranging from 6% to 15%; both HC3 databases exhibit a smaller bias than McClear. McClear offers an overall better performance for the cloud-free DNI estimates. For all-sky conditions, the relative RMSE for GHI ranges from 10% to 22%, except one station, while, for the DNI, the results are not so good for the two stations with DNI measurements.
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Validating surface downwelling solar Irradiances estimated by the McClear model under cloud-free skies in the United Arab Emirates
Solar Energy, 2015Co-Authors: Yehia Eissa, Philippe Blanc, Hosni Ghedira, Armel Oumbe, Lucien Wald, Hélène Bru, Saima Munawwar, Dominique GoffeAbstract:McClear, a fast model based on a radiative transfer solver, exploits the atmospheric properties provided by the EU-funded MACC project (Monitoring Atmospheric Composition and Climate) to estimate the surface downwelling solar Irradiances for cloud-free instances. This article presents the first validation of the McClear model for the specific climate of the United Arab Emirates where skies are frequently cloud-free but turbid. McClear accurately estimates the Global Horizontal Irradiance measured every 10 min at seven sites. The bias ranges from -9 W m-2 (-1% of the mean observed Irradiance) to +35 W m-2 (+6%). The root mean square error (RMSE) ranges from 22 W m-2 (4%) to 47 W m-2 (8%) and the coefficient of determination ranges from 0.980 to 0.990. Estimates of the direct Irradiance at normal incidence exhibit an underestimation that is attributed to the overestimation of the aerosol optical depth in the MACC data set and not accounting for the circumsolar radiation in McClear. The corresponding bias ranges from -57 W m-2 (-8%) to +6 W m-2 (+1%). The RMSE ranges from 62 W m-2 (9%) to 87 W m-2 (13%) and the coefficient of determination ranges from 0.830 to 0.863. When compared to two other models in the literature, McClear is better able to capture the temporal variability of the direct Irradiance at normal incidence. The validation results remain comparable for the Global Horizontal Irradiance.
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artificial neural network based model for retrieval of the direct normal diffuse Horizontal and Global Horizontal Irradiances using seviri images
Solar Energy, 2013Co-Authors: Yehia Eissa, Hosni Ghedira, Prashanth Reddy Marpu, Imen Gherboudj, Taha B M J Ouarda, Matteo ChiesaAbstract:Abstract A statistical model for the prediction of the solar Irradiance components, utilizing six thermal channels of the SEVIRI instrument (onboard Meteosat Second Generation satellite), is presented. Additional inputs to the model include the solar zenith angle, solar time, day number and eccentricity correction. Treating the cloud-free and cloudy observations separately, the model employs two trained artificial neural network ensembles, one for estimating the direct normal Irradiance and the other for estimating the diffuse Horizontal Irradiance. The Global Horizontal Irradiance is then computed from the model’s outputs. The model has been trained using reference data from three ground measurement stations for the full year of 2010 and tested over two independent stations for the full year of 2009. Over the two independent stations for all sky conditions, the relative root mean square errors for the direct, diffuse and Global components are 26.1%, 25.6% and 12.4%, respectively, while the relative mean bias errors are −6%, +3.6% and −2.9%, respectively. The temporal and spatial variations of the direct, diffuse and Global components are also presented for three days exhibiting different sky conditions in the year 2009.
Dazhi Yang - One of the best experts on this subject based on the ideXlab platform.
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post processing of nwp forecasts using ground or satellite derived data through kernel conditional density estimation
Journal of Renewable and Sustainable Energy, 2019Co-Authors: Dazhi YangAbstract:Global Horizontal Irradiance (GHI) forecasts by numerical weather prediction (NWP) often contain model-led bias. There is thus a strong consensus on using post-processing techniques, such as model output statistics (MOS), to correct such errors. As opposed to the conventional parametric methods, this article considers a nonparametric approach for post-processing, namely, kernel conditional density estimation (KCDE). Essentially, KCDE constructs a relationship between the bias error (difference between the NWP-based GHI forecast and measurement) and NWP output variables, such as clear-sky index, zenith angle, air temperature, humidity, or surface pressure. Hence, when a new set of explanatory variables becomes available, the conditional expectation of the bias error can be estimated. Since the ground-based GHI measurements are not available everywhere, the possibility of using satellite-derived GHI data to correct NWP forecasts is also explored. In the case study, two years of GHI forecasts made using the North American Mesoscale forecast system are corrected using both ground-measured and satellite-derived GHI references. As compared to Lorenz's fourth-degree polynomial MOS, additional 10%–16% (using ground-measured GHI) and 5%–13% (using satellite-derived GHI) reductions in the forecast error are observed at 7 test stations across the continental United States.
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forecasting of Global Horizontal Irradiance by exponential smoothing using decompositions
Energy, 2015Co-Authors: Dazhi Yang, Vishal Sharma, Li Hong Idris Lim, Lu Zhao, Aloysius W AryaputeraAbstract:Time series methods are frequently used in solar Irradiance forecasting when two dimensional cloud information provided by satellite or sky camera is unavailable. ETS (exponential smoothing) has received extensive attention in the recent years since the invention of its state space formulation. In this work, we combine these models with knowledge based heuristic time series decomposition methods to improve the forecasting accuracy and computational efficiency.
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optimal orientation and tilt angle for maximizing in plane solar irradiation for pv applications in singapore
IEEE Journal of Photovoltaics, 2014Co-Authors: Yong Sheng Khoo, Dazhi Yang, Andre M Nobre, Raghav Malhotra, Ricardo Ruther, Thomas Reindl, Armin G AberleAbstract:The performance of photovoltaic (PV) modules and systems is affected by the orientation and tilt angle, as these parameters determine the amount of solar radiation received by the surface of a PV module in a specific region. In this study, three sky models (Liu and Jordan, Klucher, and Perez et al .) are used to estimate the tilted Irradiance, which would be received by a PV module at different orientations and tilt angles from the measured Global Horizontal Irradiance (GHI) and diffuse Horizontal Irradiance (DHI) in Singapore (1.37°N, 103.75°E). Modeled results are compared with measured values from Irradiance sensors facing 60° NE, tilted at 10°, 20°, 30°, 40°, and vertically tilted Irradiance sensors facing north, south, east, and west in Singapore. Using the Perez model, it is found that a module facing east gives the maximum annual tilted irradiation for Singapore's climatic conditions. These findings are further validated by one-year comprehensive monitoring of four PV systems (tilted at 10° facing north, south, east, and west) deployed in Singapore. The PV system tilted 10° facing east demonstrated the highest specific yield, with the performance ratio close to those of other orientations.
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optimal orientation and tilt angle for maximizing in plane solar irradiation for pv applications in singapore
IEEE Journal of Photovoltaics, 2014Co-Authors: Yong Sheng Khoo, Dazhi Yang, Andre M Nobre, Raghav Malhotra, Ricardo Ruther, Thomas Reindl, Armin G AberleAbstract:The performance of photovoltaic (PV) modules and systems is affected by the orientation and tilt angle, as these parameters determine the amount of solar radiation received by the surface of a PV module in a specific region. In this study, three sky models (Liu and Jordan, Klucher, and Perez et al .) are used to estimate the tilted Irradiance, which would be received by a PV module at different orientations and tilt angles from the measured Global Horizontal Irradiance (GHI) and diffuse Horizontal Irradiance (DHI) in Singapore (1.37°N, 103.75°E). Modeled results are compared with measured values from Irradiance sensors facing 60° NE, tilted at 10°, 20°, 30°, 40°, and vertically tilted Irradiance sensors facing north, south, east, and west in Singapore. Using the Perez model, it is found that a module facing east gives the maximum annual tilted irradiation for Singapore's climatic conditions. These findings are further validated by one-year comprehensive monitoring of four PV systems (tilted at 10° facing north, south, east, and west) deployed in Singapore. The PV system tilted 10° facing east demonstrated the highest specific yield, with the performance ratio close to those of other orientations.
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evaluation of transposition and decomposition models for converting Global solar Irradiance from tilted surface to Horizontal in tropical regions
Solar Energy, 2013Co-Authors: Dazhi Yang, Panida Jirutitijaroen, Zibo Dong, Andre M Nobre, Yong Sheng Khoo, Wilfred M WalshAbstract:Abstract Many simulations and performance evaluations of solar energy systems require information on local solar Irradiance as input and the most commonly required parameter is the Global Horizontal Irradiance (GHI). Irradiance at differing geographical locations is frequently measured using sensors which are installed in the plane of existing photovoltaic (PV) arrays, whose tilt is optimized to the location. One is therefore often compelled to convert the Irradiance from an arbitrary tilt to GHI as would be measured on a Horizontal surface. The existing literature, however, focuses on using transposition and decomposition models to predict solar Irradiance on tilted surface from solar Irradiance data on Horizontal plane. In this paper, we discuss the reverse process. We use Singapore data collected at various tilts and azimuths to perform the analysis. We first evaluate the performance of various transposition and decomposition models in tropical regions. We assess constraints on each decomposition model and select an optimal model using measured GHI data as a benchmark.
Wilfred M Walsh - One of the best experts on this subject based on the ideXlab platform.
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evaluation of transposition and decomposition models for converting Global solar Irradiance from tilted surface to Horizontal in tropical regions
Solar Energy, 2013Co-Authors: Dazhi Yang, Panida Jirutitijaroen, Zibo Dong, Andre M Nobre, Yong Sheng Khoo, Wilfred M WalshAbstract:Abstract Many simulations and performance evaluations of solar energy systems require information on local solar Irradiance as input and the most commonly required parameter is the Global Horizontal Irradiance (GHI). Irradiance at differing geographical locations is frequently measured using sensors which are installed in the plane of existing photovoltaic (PV) arrays, whose tilt is optimized to the location. One is therefore often compelled to convert the Irradiance from an arbitrary tilt to GHI as would be measured on a Horizontal surface. The existing literature, however, focuses on using transposition and decomposition models to predict solar Irradiance on tilted surface from solar Irradiance data on Horizontal plane. In this paper, we discuss the reverse process. We use Singapore data collected at various tilts and azimuths to perform the analysis. We first evaluate the performance of various transposition and decomposition models in tropical regions. We assess constraints on each decomposition model and select an optimal model using measured GHI data as a benchmark.
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hourly solar Irradiance time series forecasting using cloud cover index
Solar Energy, 2012Co-Authors: Dazhi Yang, Panida Jirutitijaroen, Wilfred M WalshAbstract:Abstract We apply time series analysis to forecast next hour solar Irradiance including cloud cover effects. Three forecasting methods are proposed using different types of meteorological data as input parameters, namely, Global Horizontal Irradiance (GHI), diffuse Horizontal Irradiance (DHI), direct normal Irradiance (DNI) and cloud cover. The first method directly uses GHI to forecast next hour GHI through additive seasonal decomposition followed by an Auto-Regressive Integrated Moving Average (ARIMA) model. The second method forecasts DHI and DNI separately using additive seasonal decomposition followed by an ARIMA model and then combines the two forecasts to predict GHI using an atmospheric model. The third method considers cloud cover effects. An ARIMA model is used to predict cloud transients. GHI at different zenith angles and under different cloud cover conditions is constructed using nonlinear regression, i.e., we create a look-up table of GHI regression models for different cloud cover conditions. All three methods are tested using data from two weather stations in the USA: Miami and Orlando. It is found that forecasts using cloud cover information can improve the forecast accuracy.
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the estimation of clear sky Global Horizontal Irradiance at the equator
Energy Procedia, 2012Co-Authors: Yang Dazhi, Panida Jirutitijaroen, Wilfred M WalshAbstract:Abstract We analyse Singapore Global Horizontal Irradiance (GHI) measurements to estimate clear sky GHI in the equator region. The data consists of a one-year period of clear sky GHI at three solar Irradiance monitoring stations in Singapore, namely, First Zero Energy House in Singapore (1.3094 ∘ N, 103.9160 ∘ E) located in the east of the country, Solar Energy Research Institute of Singapore (1.3007 ∘ N, 103.7718 ∘ E) in the mid-west, and Nanyang Technology University Intelligence Systems Center (1.3436 ∘ N, 103.6792 ∘ E) in the west. Several empirical clear sky models are considered for a Singapore case study. An regression method is proposed to parameterise the model of choice for Singapore's measured GHI. The developed model is validated using GHI measurements from difference stations over different time periods.