The Experts below are selected from a list of 2319 Experts worldwide ranked by ideXlab platform
Stéphane Grieu - One of the best experts on this subject based on the ideXlab platform.
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a new approach to the real time assessment and intraday forecasting of clear sky Direct Normal Irradiance
Solar Energy, 2018Co-Authors: Rémi Chauvin, Julien Nou, Stéphane Thil, Stéphane Grieu, Julien EynardAbstract:Abstract Clear-sky Direct Normal Irradiance (DNI) is the solar power received at ground level per unit of area, at a specific location, under cloud-free conditions. Regarding Concentrating Solar Power (CSP) technologies, such conditions mean that there is no cloud between the Sun and the observer, i.e. the solar field. Since clear sky defines the nominal operating conditions of CSP plants, real-time estimates and forecasts of clear-sky DNI are key information for power plant operators tasked with the management of those plants. So, the present paper focuses first on a new algorithm for the real-time detection of clear-sky situations from DNI measurements. This algorithm makes use of the last-known clear-sky situation and requires the maximum speed at which the atmosphere becomes opaque to be evaluated. The paper also focuses on an efficient approach to the real-time assessment of clear-sky DNI. This approach combines the model developed by Ineichen and Perez with a persistence of atmospheric turbidity, taking advantage of the fact that changes in this quantity are relatively small throughout the day in comparison to changes in DNI, even when the sky is free of clouds. Performance is evaluated via a comparative study, in which empirical models are included, using one-minute data from two sites (Golden, USA, and Perpignan, France). MAE and RMSE are lower than 10 W m - 2 and 21 W m - 2 , respectively. The same approach is capable of providing accurate intraday forecasts of clear-sky DNI. It has proven to be the best compromise between accuracy and complexity (reference is a persistence of DNI) among the considered approaches, including neuro-fuzzy approaches. For a forecast horizon of 5 h,MAE ≃ 30 W m - 2 and RMSE ≃ 37 W m - 2 .
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towards the intrahour forecasting of Direct Normal Irradiance using sky imaging data
Heliyon, 2018Co-Authors: Julien Nou, Rémi Chauvin, Stéphane Thil, Stéphane Grieu, Julien EynardAbstract:Abstract Increasing power plant efficiency through improved operation is key in the development of Concentrating Solar Power (CSP) technologies. To this end, one of the most challenging topics remains accurately forecasting the solar resource at a short-term horizon. Indeed, in CSP plants, production is Directly impacted by both the availability and variability of the solar resource and, more specifically, by Direct Normal Irradiance (DNI). The present paper deals with a new approach to the intrahour forecasting (the forecast horizon Δ t f is up to 30 min ahead) of DNI, taking advantage of the fact that this quantity can be split into two terms, i.e. clear-sky DNI and the clear sky index. Clear-sky DNI is forecasted from DNI measurements, using an empirical model ( Ineichen and Perez, 2002 ) combined with a persistence of atmospheric turbidity. Moreover, in the framework of the CSPIMP (Concentrating Solar Power plant efficiency IMProvement) research project, PROMES-CNRS has developed a sky imager able to provide High Dynamic Range (HDR) images. So, regarding the clear-sky index, it is forecasted from sky-imaging data, using an Adaptive Network-based Fuzzy Inference System (ANFIS). A hybrid algorithm that takes inspiration from the classification algorithm proposed by Ghonima et al. (2012) when clear-sky anisotropy is known and from the hybrid thresholding algorithm proposed by Li et al. (2011) in the opposite case has been developed to the detection of clouds. Performance is evaluated via a comparative study in which persistence models – either a persistence of DNI or a persistence of the clear-sky index – are included. Preliminary results highlight that the proposed approach has the potential to outperform these models (both persistence models achieve similar performance) in terms of forecasting accuracy: over the test data used, RMSE (the Root Mean Square Error) is reduced of about 20 W m − 2 , with Δ t f = 15 min , and 40 W m − 2 , with Δ t f = 30 min .
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towards the short term forecasting of Direct Normal Irradiance using a sky imager
IFAC-PapersOnLine, 2017Co-Authors: Julien Nou, Rémi Chauvin, Stéphane Thil, Stéphane Grieu, Julien EynardAbstract:Abstract In a context of sustainable development, interest for Concentrating Solar Power (CSP) is growing rapidly. One of the most challenging topics is to improve solar resource assessment and forecasting in order to optimize power plant operation. Indeed, in CSP plants, electricity generation is Directly impacted by both availability and variability of the solar resource and, more specifically, by Direct Normal Irradiance (DNI). Moreover, in the framework of the CSPIMP research project, PROMES-CNRS has developped a sky imager able to provide High Dymanic Range (HDR) images. As a result, the present paper deals with the short-term forecasting of DNI using sky-imaging data. Preliminary results highlight that models (in particular based on artificial intelligence tools) that make use of the fractional cloud cover have the potential to outperform persistence models in terms of forecasting accuracy.
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A new approach for assessing the clear-sky Direct Normal Irradiance in real time
2016Co-Authors: Rémi Chauvin, Julien Nou, Stéphane Thil, Stéphane GrieuAbstract:In the present paper, a new approach is proposed to assess the clear-sky Direct Normal Irradiance (DNI) in real time. This approach combines an existing empirical model, proposed by Ineichen and Perez in 2002, with a new methodology for the computation of atmospheric turbidity. This methodology takes advantage of the fact that changes in atmospheric turbidity are relatively small throughout the day in comparison to changes in DNI, even when the sky is free of clouds. We considered data from two experimental sites (Golden, in the USA, and Perpignan, in France) and compared the proposed approach with several combinations of empirical models and ways of computing atmospheric turbidity. A polynomial of the cosine of the solar zenith angle has also been included in the comparative study. In both sites, our approach outperforms the other approaches. It has proven to be well adapted to the real-time assessment of the clear-sky DNI, in particular when the Sun is occulted by clouds during less than eight consecutive hours
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A new approach to the real-time assessment of the clear-sky Direct Normal Irradiance
Applied Mathematical Modelling, 2016Co-Authors: Julien Nou, Rémi Chauvin, Stéphane Thil, Stéphane GrieuAbstract:In a context of sustainable development, interest for concentrating solar power and concentrating photovoltaic technologies is growing rapidly. One of the most challenging topics is to improve solar resource assessment and forecasting in order to optimize power plant operation. Since clear sky defines the nominal operating conditions of the plants, improving their management requires the use in real-time of clear-sky Direct Normal Irradiance (DNI) models. Typically, accuracy is best achieved by considering water vapor and aerosol concentrations in the atmosphere separately. However, measuring such physical quantities is not easy and requires a weather station close to the considered site. When these data are not available, the attenuating effects can be modelled by atmospheric turbidity factors which can be obtained from DNI under clear-sky conditions. So, the main purpose of the present paper is to propose an efficient approach to assess the clear-sky DNI in real time. This approach combines an existing empirical model, proposed by Ineichen and Perez, with a new methodology for the computation of atmospheric turbidity. It takes advantage of the fact that changes in atmospheric turbidity are relatively small throughout the day in comparison to changes in DNI, even when the sky is free of clouds. In the present study, we considered data from two experimental sites (Golden, in the USA, and Perpignan, in France) and used a wavelet-based multi-resolution analysis as a clear-sky DNI detection tool. In addition, we compared the proposed approach with several combinations of empirical models and ways of computing atmospheric turbidity. The first model is a polynomial of the cosine of the solar zenith angle, whereas the two other models use atmospheric turbidity as an additional input. Regarding its calculation, monthly and daily mean values have been considered. Moreover, we defined a procedure in order to evaluate the accuracy of all the considered approaches. This procedure allows changes in DNI caused by clouds to be simulated using a noisy signal applied to clear-sky periods. In both sites, our approach to the real-time assessment of the clear-sky DNI outperforms the other approaches. In the worst case, the mean absolute error is reduced by 8 W m −2 in comparison to the approaches based on monthly mean values of atmospheric turbidity, and reduced by about 30 W m −2 when taking the polynomial-based model as a reference.
Robert A. Taylor - One of the best experts on this subject based on the ideXlab platform.
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mesoscale simulations of australian Direct Normal Irradiance featuring an extreme dust event
Journal of Applied Meteorology and Climatology, 2017Co-Authors: S K Mukkavilli, Abhnil Amtesh Prasad, Robert A. Taylor, Alberto Troccoli, Merlinde KayAbstract:AbstractDirect Normal Irradiance (DNI) is the main input for concentrating solar power (CSP) technologies—an important component in future energy scenarios. DNI forecast accuracy is sensitive to radiative transfer schemes (RTSs) and microphysics in numerical weather prediction (NWP) models. Additionally, NWP models have large regional aerosol uncertainties. Dust aerosols can significantly attenuate DNI in extreme cases, with marked consequences for applications such as CSP. To date, studies have not compared the skill of different physical parameterization schemes for predicting hourly DNI under varying aerosol conditions over Australia. The authors address this gap by aiming to provide the first Weather and Forecasting (WRF) Model DNI benchmarks for Australia as baselines for assessing future aerosol-assimilated models. Annual and day-ahead simulations against ground measurements at selected sites focusing on an extreme dust event are run. Model biases are assessed for five shortwave RTSs at 30- and 10-k...
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evaluating the benefits of using short term Direct Normal Irradiance forecasts to operate a concentrated solar thermal plant
Solar Energy, 2016Co-Authors: Edward W Law, Merlinde Kay, Robert A. TaylorAbstract:Abstract Past studies about using Direct Normal Irradiance (DNI) forecasts to operate a concentrated solar thermal (CST) plant have not considered intra-day forecasts. This is a critical research gap because short-term forecasts are recommended for managing variable output from renewable energy generators, including CST plants. This study evaluates the benefits of using 1-h forecasts to decide updated bids for a CST plant after making initial bids from 48-h forecasts. The benefits are represented by the financial value calculated from revenue and reserve generation (RG) payments, and the reliability calculated from the equivalent forced outage rate (EFOR). Simulating a CST plant operating in the Australian National Electricity Market for one year showed that using 1-h forecasts increases financial value by $1.04–1.13 million and reduces EFOR by 20–21% points for a 50 Megawatt (MW) CST plant with 7.5 h of storage, and increases financial value by $0.7–$0.9 million and reduces EFOR by 20–23% points for a 50 MW CST plant without storage. Reduced RG costs contributed towards 76–98% of the financial value increase for both CST plants. A CST plant without storage that uses 1-h forecasts achieves an EFOR of 10–11%, whereas a CST plant with storage that does not use 1-h forecasts achieves an EFOR of 21–22%, so using 1-h forecasts may improve reliability more than adding storage to a CST plant without storage. Using 1-h forecasts does not achieve the same total net value as a perfect 48-h forecast, but it achieves close to maximum value per unit electricity generated. Overall, CST plants should use short-term forecasts if permitted under local electricity market regulations because they can achieve higher financial value and reliability. Future studies should use short-term forecasts when allowed by the local electricity market to more accurately demonstrate the value of CST plants.
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calculating the financial value of a concentrated solar thermal plant operated using Direct Normal Irradiance forecasts
Solar Energy, 2016Co-Authors: Edward W Law, Merlinde Kay, Robert A. TaylorAbstract:Abstract This study examines the effect of Direct Normal Irradiance (DNI) forecast accuracy on the financial value of a concentrated solar thermal (CST) plant. Other factors such as electricity market regulations, plant site local climate, and operating strategy are not considered. A CST model varied over 11 combinations of solar field sizes and storage sizes is used to simulate plant operation for three forecast methods and a perfect forecast. The financial value is calculated using revenue and reserve generation payments resultant from plant operation. Results show when the root mean square error (RMSE) of a 48-h DNI forecast is between 325 and 400 W/m 2 , a 1 W/m 2 improvement increases the financial value by $400–1300 per 6 months operation for a CST plant with solar multiple between 1.25 and 2, and storage size between 0 and 20 h. Similarly, when the mean absolute error (MAE) is between 250 and 300 W/m 2 , a 1 W/m 2 improvement shows an increase of $1000–3600 per 6 months operation. If two forecast methods have similar MAE or RMSE, then the method that tends to over-predict DNI achieves higher value. For all forecast methods, increasing solar multiple or storage size increases financial value. Financial value expressed using only revenue is overstated by 14–64% compared to using both revenue and reserve generation payments, depending on the CST plant configuration and the forecast method. CST plants with small solar fields or small storage sizes gain proportionally more from investing to obtain better DNI forecasts because more accurate forecasts help these CST plants generate more electricity from the limited solar field thermal output, and use more of the limited stored thermal energy to increase revenue instead of reduce reserve generation payments caused by forecast errors.
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calculating the financial value of a concentrated solar thermal plant operated using Direct Normal Irradiance forecasts
Solar Energy, 2016Co-Authors: Robert A. TaylorAbstract:Abstract This study examines the effect of Direct Normal Irradiance (DNI) forecast accuracy on the financial value of a concentrated solar thermal (CST) plant. Other factors such as electricity market regulations, plant site local climate, and operating strategy are not considered. A CST model varied over 11 combinations of solar field sizes and storage sizes is used to simulate plant operation for three forecast methods and a perfect forecast. The financial value is calculated using revenue and reserve generation payments resultant from plant operation. Results show when the root mean square error (RMSE) of a 48-h DNI forecast is between 325 and 400 W/m2, a 1 W/m2 improvement increases the financial value by $400–1300 per 6 months operation for a CST plant with solar multiple between 1.25 and 2, and storage size between 0 and 20 h. Similarly, when the mean absolute error (MAE) is between 250 and 300 W/m2, a 1 W/m2 improvement shows an increase of $1000–3600 per 6 months operation. If two forecast methods have similar MAE or RMSE, then the method that tends to over-predict DNI achieves higher value. For all forecast methods, increasing solar multiple or storage size increases financial value. Financial value expressed using only revenue is overstated by 14–64% compared to using both revenue and reserve generation payments, depending on the CST plant configuration and the forecast method. CST plants with small solar fields or small storage sizes gain proportionally more from investing to obtain better DNI forecasts because more accurate forecasts help these CST plants generate more electricity from the limited solar field thermal output, and use more of the limited stored thermal energy to increase revenue instead of reduce reserve generation payments caused by forecast errors.
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Assessment of Direct Normal Irradiance and cloud connections using satellite data over Australia
Applied Energy, 2015Co-Authors: Abhnil Amtesh Prasad, Robert A. Taylor, Merlinde KayAbstract:Abstract Australia has some of the best solar energy resources on the planet. With a Renewable Energy Target (RET) scheme designed to ensure that 20% of Australia’s electricity comes from renewable sources by 2020, these resources are rapidly being developed. Although not yet widespread, Concentrating Solar Power (CSP) plants are expected to play a significant role in Australia’s future solar-derived electricity. The variability of Direct Normal Irradiance (DNI) is largely responsible for the fluctuations in solar energy outputs from CSP plants. The temporal and spatial variability of DNI over Australia provides an assessment of the solar resource for future deployment of CSP plants. As such, this study analyses recent trends in the hourly solar DNI resource using data from 1990 to 2012 obtained from the Bureau of Meteorology (BOM). The deseasonalized DNI anomaly trends were significant over the west, southeast and northeast of Australia for all seasons. Knowledge of these trends is extremely important for siting and the prediction of CSP plant outputs. DNI increased by 50 W m −2 over west and southeast of Australia, whereas it decreased by 100 W m −2 over northeast of Australia – representing approximately +5% and −12% deviations from the long-term averages, respectively. Seasonal analysis also showed significant DNI trends, especially during the summer and winter. Most of the changes seen in DNI over Australia were modulated by changes in cloud amount over the region. The cloud amount obtained from the International Satellite Cloud Climatology Product (ISCCP) showed high negative correlations associated with DNI anomalies over Australia. The anomaly in cloud amount is highly correlated with the Southern Oscillation Index (SOI) obtained from BOM. The strengthening convective activity over Indonesia associated with strong La Nina events modulates cloud coverage teleconnecting towards northern Australia. This increases cloud cover and lowers the DNI significantly over these regions during the summer and autumn season. Although the change in DNI associated with cloud amount is clear, the effect of change in aerosols over these years still needs to be investigated.
Merlinde Kay - One of the best experts on this subject based on the ideXlab platform.
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mesoscale simulations of australian Direct Normal Irradiance featuring an extreme dust event
Journal of Applied Meteorology and Climatology, 2017Co-Authors: S K Mukkavilli, Abhnil Amtesh Prasad, Robert A. Taylor, Alberto Troccoli, Merlinde KayAbstract:AbstractDirect Normal Irradiance (DNI) is the main input for concentrating solar power (CSP) technologies—an important component in future energy scenarios. DNI forecast accuracy is sensitive to radiative transfer schemes (RTSs) and microphysics in numerical weather prediction (NWP) models. Additionally, NWP models have large regional aerosol uncertainties. Dust aerosols can significantly attenuate DNI in extreme cases, with marked consequences for applications such as CSP. To date, studies have not compared the skill of different physical parameterization schemes for predicting hourly DNI under varying aerosol conditions over Australia. The authors address this gap by aiming to provide the first Weather and Forecasting (WRF) Model DNI benchmarks for Australia as baselines for assessing future aerosol-assimilated models. Annual and day-ahead simulations against ground measurements at selected sites focusing on an extreme dust event are run. Model biases are assessed for five shortwave RTSs at 30- and 10-k...
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evaluating the benefits of using short term Direct Normal Irradiance forecasts to operate a concentrated solar thermal plant
Solar Energy, 2016Co-Authors: Edward W Law, Merlinde Kay, Robert A. TaylorAbstract:Abstract Past studies about using Direct Normal Irradiance (DNI) forecasts to operate a concentrated solar thermal (CST) plant have not considered intra-day forecasts. This is a critical research gap because short-term forecasts are recommended for managing variable output from renewable energy generators, including CST plants. This study evaluates the benefits of using 1-h forecasts to decide updated bids for a CST plant after making initial bids from 48-h forecasts. The benefits are represented by the financial value calculated from revenue and reserve generation (RG) payments, and the reliability calculated from the equivalent forced outage rate (EFOR). Simulating a CST plant operating in the Australian National Electricity Market for one year showed that using 1-h forecasts increases financial value by $1.04–1.13 million and reduces EFOR by 20–21% points for a 50 Megawatt (MW) CST plant with 7.5 h of storage, and increases financial value by $0.7–$0.9 million and reduces EFOR by 20–23% points for a 50 MW CST plant without storage. Reduced RG costs contributed towards 76–98% of the financial value increase for both CST plants. A CST plant without storage that uses 1-h forecasts achieves an EFOR of 10–11%, whereas a CST plant with storage that does not use 1-h forecasts achieves an EFOR of 21–22%, so using 1-h forecasts may improve reliability more than adding storage to a CST plant without storage. Using 1-h forecasts does not achieve the same total net value as a perfect 48-h forecast, but it achieves close to maximum value per unit electricity generated. Overall, CST plants should use short-term forecasts if permitted under local electricity market regulations because they can achieve higher financial value and reliability. Future studies should use short-term forecasts when allowed by the local electricity market to more accurately demonstrate the value of CST plants.
-
calculating the financial value of a concentrated solar thermal plant operated using Direct Normal Irradiance forecasts
Solar Energy, 2016Co-Authors: Edward W Law, Merlinde Kay, Robert A. TaylorAbstract:Abstract This study examines the effect of Direct Normal Irradiance (DNI) forecast accuracy on the financial value of a concentrated solar thermal (CST) plant. Other factors such as electricity market regulations, plant site local climate, and operating strategy are not considered. A CST model varied over 11 combinations of solar field sizes and storage sizes is used to simulate plant operation for three forecast methods and a perfect forecast. The financial value is calculated using revenue and reserve generation payments resultant from plant operation. Results show when the root mean square error (RMSE) of a 48-h DNI forecast is between 325 and 400 W/m 2 , a 1 W/m 2 improvement increases the financial value by $400–1300 per 6 months operation for a CST plant with solar multiple between 1.25 and 2, and storage size between 0 and 20 h. Similarly, when the mean absolute error (MAE) is between 250 and 300 W/m 2 , a 1 W/m 2 improvement shows an increase of $1000–3600 per 6 months operation. If two forecast methods have similar MAE or RMSE, then the method that tends to over-predict DNI achieves higher value. For all forecast methods, increasing solar multiple or storage size increases financial value. Financial value expressed using only revenue is overstated by 14–64% compared to using both revenue and reserve generation payments, depending on the CST plant configuration and the forecast method. CST plants with small solar fields or small storage sizes gain proportionally more from investing to obtain better DNI forecasts because more accurate forecasts help these CST plants generate more electricity from the limited solar field thermal output, and use more of the limited stored thermal energy to increase revenue instead of reduce reserve generation payments caused by forecast errors.
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spatio temporal characterisation of extended low Direct Normal Irradiance events over australia using satellite derived solar radiation data
Renewable Energy, 2015Co-Authors: Ben Elliston, Abhnil Amtesh Prasad, Iain Macgill, Merlinde KayAbstract:As part of an on-going program to develop technological scenarios for 100 per cent renewable generation within the Australian National Electricity Market (NEM), we explore the degree to which concentrating solar thermal (CST) power might reliably contribute to the generation mix. We analyse satellite-derived hourly Direct Normal Irradiance data provided by the Australian Bureau of Meteorology for Australia over a 13-year period. This large data set covers sufficient time to enable us to characterise the frequency and duration of rare events such as extended periods of heavy cloud cover and hence low solar insolation over regions of Australia. The results highlight those regions with both the highest and lowest occurrence of extended periods of low DNI. They also identify regions whose correlated climatic characteristics would reduce overall CST generation variability if the plants were distributed across them. As such, the findings may assist both project developers, and long-term system planning for reserve generation capacity in future high renewable generation mixes.
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Assessment of Direct Normal Irradiance and cloud connections using satellite data over Australia
Applied Energy, 2015Co-Authors: Abhnil Amtesh Prasad, Robert A. Taylor, Merlinde KayAbstract:Abstract Australia has some of the best solar energy resources on the planet. With a Renewable Energy Target (RET) scheme designed to ensure that 20% of Australia’s electricity comes from renewable sources by 2020, these resources are rapidly being developed. Although not yet widespread, Concentrating Solar Power (CSP) plants are expected to play a significant role in Australia’s future solar-derived electricity. The variability of Direct Normal Irradiance (DNI) is largely responsible for the fluctuations in solar energy outputs from CSP plants. The temporal and spatial variability of DNI over Australia provides an assessment of the solar resource for future deployment of CSP plants. As such, this study analyses recent trends in the hourly solar DNI resource using data from 1990 to 2012 obtained from the Bureau of Meteorology (BOM). The deseasonalized DNI anomaly trends were significant over the west, southeast and northeast of Australia for all seasons. Knowledge of these trends is extremely important for siting and the prediction of CSP plant outputs. DNI increased by 50 W m −2 over west and southeast of Australia, whereas it decreased by 100 W m −2 over northeast of Australia – representing approximately +5% and −12% deviations from the long-term averages, respectively. Seasonal analysis also showed significant DNI trends, especially during the summer and winter. Most of the changes seen in DNI over Australia were modulated by changes in cloud amount over the region. The cloud amount obtained from the International Satellite Cloud Climatology Product (ISCCP) showed high negative correlations associated with DNI anomalies over Australia. The anomaly in cloud amount is highly correlated with the Southern Oscillation Index (SOI) obtained from BOM. The strengthening convective activity over Indonesia associated with strong La Nina events modulates cloud coverage teleconnecting towards northern Australia. This increases cloud cover and lowers the DNI significantly over these regions during the summer and autumn season. Although the change in DNI associated with cloud amount is clear, the effect of change in aerosols over these years still needs to be investigated.
Stéphane Thil - One of the best experts on this subject based on the ideXlab platform.
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a new approach to the real time assessment and intraday forecasting of clear sky Direct Normal Irradiance
Solar Energy, 2018Co-Authors: Rémi Chauvin, Julien Nou, Stéphane Thil, Stéphane Grieu, Julien EynardAbstract:Abstract Clear-sky Direct Normal Irradiance (DNI) is the solar power received at ground level per unit of area, at a specific location, under cloud-free conditions. Regarding Concentrating Solar Power (CSP) technologies, such conditions mean that there is no cloud between the Sun and the observer, i.e. the solar field. Since clear sky defines the nominal operating conditions of CSP plants, real-time estimates and forecasts of clear-sky DNI are key information for power plant operators tasked with the management of those plants. So, the present paper focuses first on a new algorithm for the real-time detection of clear-sky situations from DNI measurements. This algorithm makes use of the last-known clear-sky situation and requires the maximum speed at which the atmosphere becomes opaque to be evaluated. The paper also focuses on an efficient approach to the real-time assessment of clear-sky DNI. This approach combines the model developed by Ineichen and Perez with a persistence of atmospheric turbidity, taking advantage of the fact that changes in this quantity are relatively small throughout the day in comparison to changes in DNI, even when the sky is free of clouds. Performance is evaluated via a comparative study, in which empirical models are included, using one-minute data from two sites (Golden, USA, and Perpignan, France). MAE and RMSE are lower than 10 W m - 2 and 21 W m - 2 , respectively. The same approach is capable of providing accurate intraday forecasts of clear-sky DNI. It has proven to be the best compromise between accuracy and complexity (reference is a persistence of DNI) among the considered approaches, including neuro-fuzzy approaches. For a forecast horizon of 5 h,MAE ≃ 30 W m - 2 and RMSE ≃ 37 W m - 2 .
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towards the intrahour forecasting of Direct Normal Irradiance using sky imaging data
Heliyon, 2018Co-Authors: Julien Nou, Rémi Chauvin, Stéphane Thil, Stéphane Grieu, Julien EynardAbstract:Abstract Increasing power plant efficiency through improved operation is key in the development of Concentrating Solar Power (CSP) technologies. To this end, one of the most challenging topics remains accurately forecasting the solar resource at a short-term horizon. Indeed, in CSP plants, production is Directly impacted by both the availability and variability of the solar resource and, more specifically, by Direct Normal Irradiance (DNI). The present paper deals with a new approach to the intrahour forecasting (the forecast horizon Δ t f is up to 30 min ahead) of DNI, taking advantage of the fact that this quantity can be split into two terms, i.e. clear-sky DNI and the clear sky index. Clear-sky DNI is forecasted from DNI measurements, using an empirical model ( Ineichen and Perez, 2002 ) combined with a persistence of atmospheric turbidity. Moreover, in the framework of the CSPIMP (Concentrating Solar Power plant efficiency IMProvement) research project, PROMES-CNRS has developed a sky imager able to provide High Dynamic Range (HDR) images. So, regarding the clear-sky index, it is forecasted from sky-imaging data, using an Adaptive Network-based Fuzzy Inference System (ANFIS). A hybrid algorithm that takes inspiration from the classification algorithm proposed by Ghonima et al. (2012) when clear-sky anisotropy is known and from the hybrid thresholding algorithm proposed by Li et al. (2011) in the opposite case has been developed to the detection of clouds. Performance is evaluated via a comparative study in which persistence models – either a persistence of DNI or a persistence of the clear-sky index – are included. Preliminary results highlight that the proposed approach has the potential to outperform these models (both persistence models achieve similar performance) in terms of forecasting accuracy: over the test data used, RMSE (the Root Mean Square Error) is reduced of about 20 W m − 2 , with Δ t f = 15 min , and 40 W m − 2 , with Δ t f = 30 min .
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towards the short term forecasting of Direct Normal Irradiance using a sky imager
IFAC-PapersOnLine, 2017Co-Authors: Julien Nou, Rémi Chauvin, Stéphane Thil, Stéphane Grieu, Julien EynardAbstract:Abstract In a context of sustainable development, interest for Concentrating Solar Power (CSP) is growing rapidly. One of the most challenging topics is to improve solar resource assessment and forecasting in order to optimize power plant operation. Indeed, in CSP plants, electricity generation is Directly impacted by both availability and variability of the solar resource and, more specifically, by Direct Normal Irradiance (DNI). Moreover, in the framework of the CSPIMP research project, PROMES-CNRS has developped a sky imager able to provide High Dymanic Range (HDR) images. As a result, the present paper deals with the short-term forecasting of DNI using sky-imaging data. Preliminary results highlight that models (in particular based on artificial intelligence tools) that make use of the fractional cloud cover have the potential to outperform persistence models in terms of forecasting accuracy.
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A new approach for assessing the clear-sky Direct Normal Irradiance in real time
2016Co-Authors: Rémi Chauvin, Julien Nou, Stéphane Thil, Stéphane GrieuAbstract:In the present paper, a new approach is proposed to assess the clear-sky Direct Normal Irradiance (DNI) in real time. This approach combines an existing empirical model, proposed by Ineichen and Perez in 2002, with a new methodology for the computation of atmospheric turbidity. This methodology takes advantage of the fact that changes in atmospheric turbidity are relatively small throughout the day in comparison to changes in DNI, even when the sky is free of clouds. We considered data from two experimental sites (Golden, in the USA, and Perpignan, in France) and compared the proposed approach with several combinations of empirical models and ways of computing atmospheric turbidity. A polynomial of the cosine of the solar zenith angle has also been included in the comparative study. In both sites, our approach outperforms the other approaches. It has proven to be well adapted to the real-time assessment of the clear-sky DNI, in particular when the Sun is occulted by clouds during less than eight consecutive hours
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A new approach to the real-time assessment of the clear-sky Direct Normal Irradiance
Applied Mathematical Modelling, 2016Co-Authors: Julien Nou, Rémi Chauvin, Stéphane Thil, Stéphane GrieuAbstract:In a context of sustainable development, interest for concentrating solar power and concentrating photovoltaic technologies is growing rapidly. One of the most challenging topics is to improve solar resource assessment and forecasting in order to optimize power plant operation. Since clear sky defines the nominal operating conditions of the plants, improving their management requires the use in real-time of clear-sky Direct Normal Irradiance (DNI) models. Typically, accuracy is best achieved by considering water vapor and aerosol concentrations in the atmosphere separately. However, measuring such physical quantities is not easy and requires a weather station close to the considered site. When these data are not available, the attenuating effects can be modelled by atmospheric turbidity factors which can be obtained from DNI under clear-sky conditions. So, the main purpose of the present paper is to propose an efficient approach to assess the clear-sky DNI in real time. This approach combines an existing empirical model, proposed by Ineichen and Perez, with a new methodology for the computation of atmospheric turbidity. It takes advantage of the fact that changes in atmospheric turbidity are relatively small throughout the day in comparison to changes in DNI, even when the sky is free of clouds. In the present study, we considered data from two experimental sites (Golden, in the USA, and Perpignan, in France) and used a wavelet-based multi-resolution analysis as a clear-sky DNI detection tool. In addition, we compared the proposed approach with several combinations of empirical models and ways of computing atmospheric turbidity. The first model is a polynomial of the cosine of the solar zenith angle, whereas the two other models use atmospheric turbidity as an additional input. Regarding its calculation, monthly and daily mean values have been considered. Moreover, we defined a procedure in order to evaluate the accuracy of all the considered approaches. This procedure allows changes in DNI caused by clouds to be simulated using a noisy signal applied to clear-sky periods. In both sites, our approach to the real-time assessment of the clear-sky DNI outperforms the other approaches. In the worst case, the mean absolute error is reduced by 8 W m −2 in comparison to the approaches based on monthly mean values of atmospheric turbidity, and reduced by about 30 W m −2 when taking the polynomial-based model as a reference.
José V. Boscà - One of the best experts on this subject based on the ideXlab platform.
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Validation of a method to estimate Direct Normal Irradiance of UVA and PAR bands from global horizontal measurements for cloudless sky conditions in Valencia, Spain, by a measurement campaign
Theoretical and Applied Climatology, 2011Co-Authors: María-antonia Serrano, José V. BoscàAbstract:A method is proposed to provide measurement of Direct Normal solar Irradiance of bands with wavelength ranges (315–400 nm, 400–700 nm) from measurements of global horizontal band Irradiance for cloudless sky conditions in Valencia. Global and Normal Direct Irradiance data for every air mass were obtained by applying the SMART2 model to the atmosphere of Valencia. The Direct Normal to global Irradiance ratio was parameterized versus the relative optical air mass. A measurement campaign of global horizontal and diffuse Irradiance of UVA and PAR bands was carried out in Valencia, after which, the inferred Direct Normal Irradiance was compared with those provided by the method. The result of the comparison shows that the method is acceptably accurate. The proposed model tends to underestimate the Direct Normal Irradiance of the UVA band by 6%, although for values below 25 W/m^2 the model overestimates the Direct Irradiance by 6%, while for values above 25 W/m^2 the model underestimates it by 10%. The other two error estimators used ranging from 11% to 15% are similar in the defined interval measurements in relation to the whole UVA band. Regarding the PAR band, the model overestimates the Direct Normal Irradiance of the PAR band by only 2.2%. With this, the results of the PAR band are more conclusive, as it has been found that for Direct Normal Irradiance values higher than 280 W/m^2 the MBE error is almost zero and the other two estimator errors are small, about 5%.
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Validation of a method to estimate Direct Normal Irradiance of UVA and PAR bands from global horizontal measurements for cloudless sky conditions in Valencia, Spain, by a measurement campaign
Theoretical and Applied Climatology, 2010Co-Authors: María-antonia Serrano, José V. BoscàAbstract:A method is proposed to provide measurement of Direct Normal solar Irradiance of bands with wavelength ranges (315-400 nm, 400-700 nm) from measurements of global horizontal band Irradiance for cloudless sky conditions in Valencia. Global and Normal Direct Irradiance data for every air mass were obtained by applying the SMART2 model to the atmosphere of Valencia. The Direct Normal to global Irradiance ratio was parameterized versus the relative optical air mass. A measurement campaign of global horizontal and diffuse Irradiance of UVA and PAR bands was carried out in Valencia, after which, the inferred Direct Normal Irradiance was compared with those provided by the method. The result of the comparison shows that the method is acceptably accurate. The proposed model tends to underestimate the Direct Normal Irradiance of the UVA band by 6%, although for values below 25 W/m2 the model overestimates the Direct Irradiance by 6%, while for values above 25 W/m2 the model underestimates it by 10%. The other two error estimators used ranging from 11% to 15% are similar in the defined interval measurements in relation to the whole UVA band. Regarding the PAR band, the model overestimates the Direct Normal Irradiance of the PAR band by only 2.2%. With this, the results of the PAR band are more conclusive, as it has been found that for Direct Normal Irradiance values higher than 280 W/m2 the MBE error is almost zero and the other two estimator errors are small, about 5%. © 2010 Springer-Verlag.This work was supported by the Spanish Government through MEC grant MAT2009-14625-C03-03, and is a part of the activities of the Grup d'Optoelectronica i Semiconductors of the Polytechnic University of Valencia. The translation of this paper was funded by the Universidad Politecnica de Valencia, Spain.Serrano Jareño, MA.; Bosca Berga, JV. (2011). Validation of a method to estimate Direct Normal Irradiance of UVA and PAR bands from global horizontal measurements for cloudless sky conditions in Valencia, Spain, by a measurement campaign. Theoretical and Applied Climatology. 103(1):95-101. doi:10.1007/s00704-010-0284-9S95101103