Clear-sky Direct Normal Irradiance Research Articles

Intrahour Direct Normal Irradiance Forecasting Based on Sky Image Processing and Time-Series Analysis

The present paper exhibits a hybrid model for intrahour forecasting of direct normal irradiance (DNI). It combines a knowledge-based model, which is used for clear-sky DNI forecasting from DNI measurements, with a machine-learning-based model, that evaluates the impact of atmospheric disturbances on the solar resource, through the processing of high dynamic range sky images provided by a ground-based camera. The performance of the hybrid model is compared with that of two machine learning models based on past DNI observations only. The results highlight the pertinence of combining knowledge-based models with data-driven models, and of integrating sky-imaging data in the DNI forecasting process. Parts of this paper were published as journal articleKarout, Y.; Thil, S.; Eynard, J.; Guillot, E.; Grieu, S. Hybrid intrahour DNI forecast model based on DNI measurements and sky-imaging data. Solar Energy. 2023, 249, 541-558. https://doi.org/10.1016/j.solener.2022.11.032

Performance assessment of clear-sky solar irradiance predictions using state-of-the-art radiation models and input atmospheric data from reanalysis or ground measurements

In this work, the performance of clear-sky direct normal irradiance (DNI) and global horizontal irradiance (GHI) predictions generated with three state-of-the-art solar radiation models with different degrees of complexity is assessed by comparison with high-quality measured irradiance data at Évora, Portugal. The libRadtran, SMARTS, and REST2 radiation models are alternatively operated using input data from three different data sources: co-located AERONET ground-based measurements, and CAMS and MERRA-2 gridded reanalysis data. For these nine combinations (three models and three data sources), the results are assessed using five statistical indicators, namely mean bias error (MBE), root mean square error (RMSE), fractional bias (FB), fractional gross error (FGE), and coefficient of determination (R2). Overall, it is found that AERONET is the data source that provides the best DNI estimates. In general, libRadtran and SMARTS produced closer estimates to the ground-based DNI observations. For GHI, however, no firm conclusion can be drawn regarding the best data source. MERRA-2 produces better estimates in combination with libRadtran and SMARTS according to all statistical indicators except R2, whereas AERONET is to be preferred according to FB, FGE, and R2 when using REST2. Curiously, the latter generates better GHI estimates despite being the simplest model. Overall, it is concluded that the best combinations of model/data source to estimate DNI are either libRadtran/MERRA-2 (according to MBE and FB) and SMARTS/AERONET (according to RMSE, FGE, and R2). In the case of GHI, the best combinations are REST2/AERONET (according to FB, FGE, and R2) and REST2/MERRA-2 (according to MBE and RMSE).

Impacts of the Aerosol Representation in WRF-Solar Clear-Sky Irradiance Forecasts over CONUS

Abstract Aerosol optical depth (AOD) is a primary source of solar irradiance forecast error in clear-sky conditions. Improving the accuracy of AOD in NWP models like WRF will thus reduce error in both direct normal irradiance (DNI) and global horizontal irradiance (GHI), which should improve solar power forecast errors, at least in cloud-free conditions. In this study clear-sky GHI and DNI was analyzed from four configurations of the WRF-Solar model with different aerosol representations: 1) the default Tegen climatology, 2) imposing AOD forecasts from the GEOS-5 model, 3) imposing AOD forecasts from the Copernicus Atmosphere Monitoring Service (CAMS) model, and 4) the Thompson–Eidhammer aerosol-aware water/ice-friendly aerosol climatology. More than 8 months of these 15-min output forecasts are compared with high-quality irradiance observations at NOAA SURFRAD and Solar Radiation (SOLRAD) stations located across CONUS. In general, WRF-Solar with GEOS-5 AOD had the lowest errors in clear-sky DNI, while WRF-Solar with CAMS AOD had the highest errors, higher even than the two aerosol climatologies, which is consistent with validation of the four AOD550 datasets against AERONET stations. For clear-sky GHI, the statistics differed little between the four models, as expected because of the lesser sensitivity of GHI to aerosol loading. Hourly average clear-sky DNI and GHI were also analyzed, and they were additionally compared with CAMS model output directly. CAMS irradiance performed competitively with the best WRF-Solar configuration (with GEOS-5 AOD). The markedly different performance of CAMS versus WRF-Solar with CAMS AOD indicates that CAMS is apparently less sensitive to AOD550 than WRF-Solar is. Significance Statement Particles in the atmosphere called aerosols, which can include dust, smoke, sea salt, sulfates, black carbon, and organic carbon, absorb and scatter incoming sunlight. Improving the representation of aerosols in numerical weather prediction models reduces forecast errors in solar irradiance at ground level, particularly direct normal irradiance, during cloud-free conditions. This in turn should result in improved accuracy of solar power forecasts, especially for concentrated solar power (CSP) plants. CSP plants tend to be built in more arid, less cloudy regions that are also prone to dust loading, so accurate aerosol forecasts are particularly relevant. Comparing four representations of aerosols in the WRF-Solar model over eight months of forecasts across the United States reveals substantial differences in clear-sky irradiance forecast skill.

Low‐cost outdoor spectral irradiances determination and its application on Concentrator Photovoltaic module power rating and energy yield calculation

AbstractIn this work, a method to calculate the aerosol content in the atmosphere and the prevailing spectral irradiance of the sunlight is introduced. This method does not aim for low measurement uncertainty but for cost effectiveness and does not require sophisticated equipment. The bases of the method are the clear sky direct normal irradiance (DNI) and a look‐up table calculated with the software tool SMARTS2. The ratio of clear sky DNI to the prevailing DNI in combination with the look‐up table allows the determination of aerosol optical depth (AOD), spectral matching ratios (SMR), and spectral irradiances. Besides the prevailing DNI, the following ambient condition parameters are required: Air pressure, relative humidity, and ambient temperature. This means a weather station and a pyrheliometer are sufficient for the determination of the prevailing spectral irradiance. Obviously, this method cannot guarantee the same accuracy as conventional spectral irradiance measurement methods (e.g., using a spectroradiometer). However, in this work, we demonstrate the potential of this new method as a fall‐back strategy for missing spectral irradiance data. This method is worth using when (C)PV module power output data needs to be evaluated in dependence of the composition of the spectral irradiance, but no regular spectral irradiance data are available. In this paper, the AOD and SMR values determined with the introduced method are compared to measurement data with common measurement devices demonstrating a satisfying agreement. The spectral irradiances calculated with this method are successfully tested for using as the basis for energy yield calculations and for the determination of the CPV module power output at Concentrator Standard Conditions according to IEC 62670‐1.

Hybrid intrahour DNI forecast model based on DNI measurements and sky-imaging data

Short-term forecasting of direct normal irradiance (DNI), which refers to photons that did not interact with the atmosphere on their way to the observer, is of great interest in the advanced management of concentrated solar power systems. So, this paper exhibits a hybrid model dedicated to the intrahour forecasting of DNI (horizons are 5, 10, and 15 min). This hybrid model combines a knowledge-based model with a machine learning model: the knowledge-based model is used for clear-sky DNI forecasting from DNI measurements; the machine learning model evaluates the impact of the atmospheric disturbances on the solar resource, through the processing of high dynamic range sky images provided by a ground-based camera. The hybrid model is evaluated by comparing its performance, for different sky conditions, with that of two machine learning models based on past DNI observations only. The results highlight the pertinence of combining knowledge-based models with data-driven models, and of integrating sky-imaging data in the DNI forecasting process: the proposed hybrid model successfully manages clear-sky, overcast, and mixed situations. Regardless of the forecast horizon, the hybrid model outperforms the reference models both in normalized root mean squared error and in DNI ramp detection.

Effects of spatial scale of atmospheric reanalysis data on clear-sky surface radiation modeling in tropical climates: A case study for Singapore

Solar resource assessments most generally require atmospheric information, which is customarily acquired from gridded datasets. The spatial scale mismatch problem, i.e., the difference in spatial representativeness of gridded data and in situ measurements, therefore becomes relevant. This study examines how the gridded data used as inputs to clear-sky radiation models can affect their performance at urban scale. The tropical island of Singapore is selected for the case study. Aerosol optical depth at 550 nm (AOD550), Å ngström exponent (AE), and precipitable water (PW) from both the MERRA-2 reanalysis and ground-based stations (AERONET and SuomiNet) are collected between 2013–2020. Firstly, it is found that, relatively to the AERONET ground truth, the bias in MERRA-2’s AOD550 is more prominent than that in AE or PW. Next, the bias propagation from the gridded inputs (AOD550, AE, and PW) to clear-sky radiation predictions is explored using various models. The estimated clear-sky direct normal irradiance (DNIcs) is more sensitive to AOD550 variation than the clear-sky global horizontal irradiance (GHIcs). Six clear-sky radiation models, five of which accept MERRA-2 gridded inputs, are compared with each other, and with the in situ irradiance measurements recorded at 9 sites. The inter-model difference across Singapore is remarkably consistent because the whole island fits inside a single MERRA-2 grid cell. Under high-AOD550 situations, however, the inter-model deviation becomes large for both GHIcs and DNIcs. The conventional model-versus-measurement comparison shows that each model achieves very different site-to-site performance, largely because the spatially-averaged inputs cannot fully represent the micro-climatic variability. Relatively speaking, no clear-sky radiation model significantly outperforms its peers. The simple MAC2 model and the empirical (locally derived) Yang GHIcs-only model are recommended for Singapore.

Improved turbidity estimation from local meteorological data for solar resourcing and forecasting applications

This work presents a new method to estimate atmospheric turbidity with improved accuracy in estimating clear-sky irradiance. The turbidity is estimated by machine learning algorithms using commonly measured meteorological data including ambient air temperature, relative humidity, wind speed and atmospheric pressure. The estimated turbidity is then served as the Linke Turbidity input to the Ineichen-Perez clear-sky model to estimate clear-sky global horizontal irradiance (GHI) and direct normal irradiance (DNI). When compared with the original Ineichen-Perez model which uses interpolated turbidity from the monthly climatological means, our turbidity estimation better captures its daily, seasonal, and annual variations. When using the improved turbidity estimation in the Ineichen-Perez model, the root mean square error (RMSE) of clear-sky GHI is reduced from 24.02 W m−2 to 9.94 W m−2. The RMSE of clear-sky DNI is deceased from 76.40 W m−2 to 29.96 W m−2. The presented method is also capable to estimate turbidity in partially cloudy days with improved accuracy, evidenced by that the corresponding estimated clear-sky irradiance has smaller deviation from measured irradiance in the cloudless time instants. In sum, the proposed method brings new insights about turbidity estimation in both clear and partially cloudy days, providing support to solar resourcing and forecasting.

Accuracy of satellite-derived solar direct irradiance in Southern Spain and Switzerland

ABSTRACT We present a validation study of direct normal irradiance (DNI) estimates from HelioMont with ground-based measurements from two European sites for the year 2015. The HelioMont algorithm infers irradiance with data from the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument as the primary source of information on clouds, and data from models or reanalysis for other influential input parameters. The validation sites are the Plataforma Solar de Almería (PSA), a solar power research facility in Southern Spain characterized by arid conditions and the Swiss Baseline Surface Radiation Network (BSRN) site of Payerne, characterized by a much more frequent cloud coverage. Our analysis shows the importance of separately evaluating the quality of (1) the clear-sky irradiance computation and (2) the determination of the cloud effect. We also specifically investigate the cloud modification factor (CMF) using a validation CMF derived from ground-based data, giving us more insight into event-by-event agreement between HelioMont estimates and measured irradiances. The clear-sky HelioMont DNI uncertainty is mainly influenced by the aerosol optical depth (AOD) input data. Using the original AOD input (a 2008 climatology based on data from the Aerosol Comparisons between Observations and Models project) leads to large negative biases of 115 W m−2 to 145 W m−2. Using AOD from the Copernicus Atmosphere Monitoring Service (CAMS) allows reducing these biases to 15 W m−2 to 25 W m−2 (2% to 3%) with a dispersion of ±12% to ±15%, which is the HelioMont clear-sky DNI expanded uncertainty when using CAMS AOD. Using ground-measured AOD reduces this uncertainty to ±5% to ±6.5%, which is probably the limit of what is achievable with HelioMont. For all-sky comparisons, mean biases were between about −5 W m−2 and 55 W m−2 (depending on AOD input and station), while the root-mean-square deviation (RMSD) was between about 175 W m−2 and 195 W m−2. Our validation method yielded correlation between HelioMont and validation CMF between 0.79 and 0.92 (Pearson’s correlation coefficient r), while RMSD was between 0.18 and 0.24. The computation of the cloud effect is the part of HelioMont that is the main source of uncertainty. Systematic errors were identified (underestimation of the number of near-zero DNI and overestimation of the number of clear-sky cases) and solving them may lead to substantial improvement.

Worldwide performance assessment of 95 direct and diffuse clear-sky irradiance models using principal component analysis

Accurate estimations of clear-sky direct normal irradiance (DNIcs) and diffuse horizontal irradiance (DIFcs) are crucial in solar resources assessment. This study examines 95 and 88 popular clear-sky irradiance models for their worldwide estimation capability of DNIcs and DIFcs, respectively. Atmospheric inputs are from MERRA-2 reanalysis and irradiance observations for validation are extracted from 100 ground stations across five major Köppen-Geiger climate zones. They consist of 24 million 1-min measurements of DINcs and 18.7 million 1-min measurements of DIFcs after quality control and clear-sky detection, during the 5-year period 2015–2019. Using principal component analysis, the performance of each clear-sky irradiance model is ranked separately among the five climate zones, as well as given a global rank. For the Equatorial, Arid, Temperate, Cold and Polar climates, it is found that Heliosat1-I, ESRA-I, REST2v9.1, REST2v9.1 and CLS, respectively, are the best DNIcs models, while Modified Iqbal-C, Heliosat1-R, Calinoiu, Modified Iqbal-C and PSI-REST are the best DIFcs models. On a global worldwide basis, the three top-ranking models are REST2v9.1, REST2v5 and MMAC-V2 for DNIcs, and Modified Iqbal-C, PSI-REST and MRMv5 for DIFcs. The results and rankings presented are strictly relative to MERRA-2 input data, and should not be extrapolated to results from alternate sources of atmospheric data. This detailed validation exercise revealed inconsistent performance of many models across different climates, possibly due to insufficient training and/or over-fitting of empirical relationships. Codes of all clear-sky irradiance models in the public domain are available online on the Github repository JamieMBright/clear-sky-models.

Clear-sky direct normal irradiance estimation based on adjustable inputs and error correction

The accurate estimation of direct normal irradiance (DNI) under clear sky conditions plays an important role in the concentrated solar thermal plant. A hybrid model with adjustable inputs is proposed to calculate the clear-sky DNI, including a base clear-sky model and an error-correction model. The base clear-sky model is able to estimate the clear-sky DNI at any place with only the local date and location information, and the error-correction model serves as a supplementary to improve the calculating accuracy with available meteorological data. The error-correction model effectively integrates a linear part and a nonlinear part, and its inputs are adjustable according to the available meteorological observations. Several experiments have been conducted to evaluate the performance of the proposed model with data from three observation stations provided by the National Renewable Energy Laboratory open database. The results show that the hybrid model is able to provide great improvement over the base clear-sky model with 28%–70% on normalized root mean square error, and it also performs better than those using a linear or nonlinear error correction model. It is concluded that the performance of the hybrid model is comparable with other published methods in calculating the clear-sky DNI with concrete statistics.

Determination of cloud transmittance for all sky imager based solar nowcasting

The demand for accurate solar irradiance nowcast increases together with the rapidly growing share of solar energy within our electricity grids. Intra-hour variabilities, mainly caused by clouds, have a significant impact on solar power plant dispatch and thus on electricity grids. All sky imager (ASI) based nowcasting systems, with a high temporal and spatial resolution, can provide irradiance nowcasts that can help to optimize CSP plant operation, solar power plant dispatch and grid operation. The radiative effect of clouds is highly variable and depends on micro- and macrophysical cloud properties. Frequently, nowcasting systems have to measure/estimate the radiative effect during complex multi-layer conditions with strong variations of the optical properties between individual clouds.We present a novel approach determining cloud transmittance from measurements or from correlations of transmittance with cloud height information. The cloud transmittance is measured by a pyrheliometer when shaded, as the ratio of shaded direct normal irradiance (DNI) and clear sky DNI. However, for most clouds, direct transmittance measurements are not available, as these clouds are not shading the used pyrheliometers. These clouds receive an estimated transmittance value based on (1) their height, (2) results of a probability analysis with historical cloud height and transmittance measurements as well as (3) recent transmittance measurements and their corresponding cloud height. Cloud heights are measured by a stereoscopic approach utilizing two ASIs. We discuss site dependencies of the presented transmittance estimation method and the potential integration of automatic cloud classification approaches.We validated the cloud transmittance estimation over two years (2016 and 2017) and compare the probabilistic cloud transmittance estimation approach with four simple approaches. The overall mean-absolute deviation (MAD) and root-mean-square deviation (RMSD) are 0.11 and 0.16 respectively for transmittance. The deviations are significantly lower for optically thick or thin clouds and larger for clouds with moderate transmittance between 0.18 and 0.585.Furthermore we validated the overall DNI forecast quality of the entire nowcasting system, using this transmittance estimation method, over the same data set with three spatially distributed pyrheliometers. Overall deviations of 13% and 21% are reached for the relative MAD and RMSD with a lead time of 10 min. The effects of the chosen data set on the validation results are demonstrated by means of the skill score.

Real-time Clear-sky Model and Cloud Cover for Direct Normal Irradiance Prediction

Direct normal irradiance (DNI) prediction is of great significance for the concentrated solar power (CSP) generation and grid work. The clouds are the main cause of DNI intensity reduction and drastic changes. Therefore, the real-time clear-sky DNI model coupled with cloud cover is proposed for short-term DNI prediction in this study. The Linke turbidity coefficient is used to develop a clear-sky model, and real-time adjustment of the coefficients is achieved by identifying clear-sky period. Then, the theoretical clear-sky value of current day can be obtained. By the combination of the historical cloud cover and theoretical clear-sky value, Auto Regressive Moving Average (ARMA) and Artificial Neural Network (ANN) are used to develop linear and nonlinear models. Experiment with the data in the National Renewable Energy Laboratory (NREL) database, the simulation results show that this approach using cloud cover and clear-sky information can improve the forecasting accuracy.

A Data-driven Clear Sky Model for Direct Normal Irradiance

Solar energy is an ideal new energy source for power systems. In order to integrate solar energy into the power grid, an evaluation of the irradiance input to solar power systems is required in many applications. Clear-sky models describe the maximum input of solar power systems, which are particularly important for solar irradiance forecasting and numerical weather prediction. The existing clear sky models are empirical or semi-empirical models. In order to make use of the information in historical big data, a data-driven clear sky model for direct normal irradiance is proposed in this paper. Firstly, a clear sky detection algorithm is designed to collect clear-sky data from historical big data automatically. After that, the weighted k-Nearest Neighbors regression algorithm is used to calculate clear-sky direct normal irradiance by historical clear-sky data. The database of the National Renewable Energy Laboratory (NREL) is used for experiments to evaluate the performance of the proposed model, compared with other three clear sky models. The results of the experiments show that the proposed model is more accurate than other three clear sky models.

A new approach to the real-time assessment and intraday forecasting of clear-sky direct normal irradiance

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 10Wm-2 and 21Wm-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 5h,MAE≃30Wm-2 and RMSE≃37Wm-2.

Towards the intrahour forecasting of direct normal irradiance using sky-imaging data

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 Δtf is up to 30min 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 20Wm−2, with Δtf=15min, and 40Wm−2, with Δtf=30min.

Prediction of the day-ahead clear-sky downwelling surface solar irradiances using the REST2 model and WRF-CHIMERE simulations over the Arabian Peninsula

The forecasting of the downwelling surface solar irradiances is required by the operators of solar powered plants to predict the power output of the plant. The purpose of this study is to use aerosol optical depth and columnar water vapor forecasts by the chemistry transport model CHIMERE, coupled with the Weather Research and Forecast (WRF) model, as inputs in the REST2 model to predict the day-ahead clear-sky global horizontal, direct normal and diffuse horizontal irradiances at hourly steps. A dataset of quality-assured and cloud-screened surface solar irradiance measurements collected from 44 sites scattered throughout Saudi Arabia was used to validate the predicted surface solar irradiances. The validation for the day-ahead forecasting of the clear-sky global horizontal irradiance for the data of all 44 stations combined exhibits a bias (relative to the mean reference value) of 0%, a relative root mean square error of 4% and a correlation coefficient of 0.993. Respectively, the clear-sky direct normal irradiance exhibits values of 0%, 15% and 0.769, while the clear-sky diffuse horizontal irradiance values are 0%, 35% and 0.542. The errors in the inputs are also investigated. The results are satisfactory and pave the way to the inclusion of the attenuation due to clouds with the final aim of predicting the surface solar irradiance under all-sky conditions.

Intra-hour direct normal irradiance forecasting through adaptive clear-sky modelling and cloud tracking

Hybrid fossil-solar power cycles possess high solar-to-electric conversion efficiency and the potential to provide stable power output without the need for costly storage systems. However, the power output of the concentrating solar power (CSP) component of the plant can fluctuate in sync with intermittent direct normal irradiance (DNI). For hybrid plants, the controllable fossil-fuel unit can be used to compensate for shortfalls in CSP output during periods of light DNI intermittency or provide the entire plant output during periods of high DNI intermittency. This fossil-fuel unit requires 5–10 min to ramp its power output up or down to perform these balancing functions. Thus, by accurately predicting future DNI, and hence CSP output, the fossil-fuel unit can guarantee stable plant-wide power output during periods of intermittency.This paper develops an intra-hour DNI prediction system using a ground-based cloud motion vector (CMV) framework and real-time DNI measurements. This system comprises a clear-sky DNI model and a cloud fraction prediction algorithm. The presented CSM is based on the Ineichen model (Ineichen, 2008), where the model parameters are adaptively estimated from identified clear-sky DNI measurements over a moving window. For the cloud fraction prediction model, this paper presents an enhanced “sector-ladder” method (Quesada-Ruiz et al., 2014) that uses the weighted mean of circular quantities (Fisher, 1995) and autoregressive filtering to improve cloud flow predictions. Furthermore, a method to forewarn against periods of high DNI intermittency using the generated DNI predictions is presented.The proposed DNI prediction system is evaluated using 37 days of sky-camera images and DNI data collected over the summer of 2014/2015 at the University of Queensland. Over all test days, the adaptive CSM has an average root mean square error of 3.06%, which represents a 19% improvement over a CSM that uses the optimal model parameters from the previous day’s data. Additionally, the modifications to the cloud flow prediction algorithm (the sector-ladder method) are shown to improve the cloud velocity prediction accuracy by a factor of seven over a period of visually determined constant cloud velocity. We find the overall prediction accuracy of the DNI prediction system to be statistically similar to the accepted short-term benchmark of persistence; however, it performs more consistently over a range of weather conditions and is able to forewarn against periods of impending intermittency with 93% accuracy. The latency from data collection to prediction is less than 30 s, making the method eminently suitable for real-time applications.

Identifying periods of clear sky direct normal irradiance

When modeling the effect of the cloud transients in the Direct Normal Insolation (DNI), it is particularly relevant to identify those moments in which there are no clouds between the observer and the sun. In this paper, we present a simple algorithm for offline detection of situations where the sun path to the observer is not obstructed by any cloud. The algorithm is based on the characterization of the relations between the measured and the clear sky curves. The clear sky identification module consists of three evaluation and detection metrics: hourly mean, slope, and line length criterion. All of them rely on the assessment of the measured data against the clear sky generated data. The conjunction of the fulfillment of the three criteria leads to the clear sky hour identification. We validate our algorithm by comparing our results with those obtained from a recently published clear sky detection algorithm that uses high temporal resolution Global Horizontal Irradiation (GHI) as the input. We obtain a 98% agreement when having more than 50 min identified as clear.

Performance simulation of the central-receiver solar concentration system based on typical meteorological year data

When solar radiation is utilized as a high-temperature heat source, performance simulation of solar concentration systems is important, especially for a central-receiver system that involves a large number of sun-tracking reflectors. To account for the intermittent nature of solar radiation, an annual performance based on 8760 hourly simulations is desirable. Therefore, this study focuses on a more realistic simulation of a central-receiver concentrator system with the Typical meteorological year (TMY) data that typify characteristic meteorological conditions at a specific location. Optical efficiency simulations of the Daegu central-receiver concentrator system were performed with a Monte Carlo ray-tracing method, and the Direct normal irradiance (DNI) in the TMY data set generated from the database of Korea Meteorological Administration (KMA) was evaluated. The TMY data in Daegu show that 80 % of the DNI values are smaller than 500 W/m2 and the annual insolation of 931 kWh/m2/year corresponds to approximately one third of that calculated from the clear-sky DNI model. Monthly optical efficiencies range from 67.8 % in October to 63.2 % in June in the base case. In contrast to the difference in solar insolation, the optical efficiencies based on the clear-sky model are slightly smaller than the efficiencies based on the TMY data, with deviations within approximately 1 %. The small difference implies that the clear-sky model can be substituted when simulation or optimization of the optical efficiency is of interest.

Clear-sky model for wavelet forecast of direct normal irradiance

Direct normal irradiance (DNI) is vital for concentrated solar thermal plants, and its value under clear sky condition is usually used as an important input of some forecasting models for solar irradiance. First of all, a semi-empirical model is developed for calculating the clear-sky DNI, and it is used to transform DNI into clear-sky index to remove the impact of the sun's position on DNI. Then, a wavelet forecasting model, using the clear-sky index as input, is proposed for estimating the inter-hour DNI under any sky conditions. The non-stationary series of the clear-sky index is decomposed by wavelet technology to obtain four sub-series with different frequencies, and forecasting sub-models are constructed according to the features of corresponding sub-series, respectively. Finally, model validation is conducted with data from the open database of the National Renewable Energy Laboratory. The result shows that the performance of the clear-sky model is satisfactory comparing with some other published clear-sky models, and that the wavelet forecasting model achieves great performance with nMAE = 0.84–7.66% and nRMSE = 1.89–10.99% for four different stations and also outperforms other published forecasting models.