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DNI assessment is essential for developing any CSP projects. In this manuscript, an approach is made for the analysis and estimation of DNI by developing the empirical models for India. The historical data of around 15 years (1986-2000) of DNI and cloud cover (CC) from six representative locations of major climatic zones namely Bhopal, Jaipur, Srinagar, Patna, New Delhi & Thiruvanantpuram have been employed. From these locations, first four have been used to develop the models correlating DNI and cloud cover; however, rest two were used to evaluate their validity. The standard statistical predictor's viz. CoD, MPE, MBE, RMSE, skewness, kurtosis & chi-square were applied to evaluate these empirical models. The CoD was observed greater than 0.8 while performance criteria have low and acceptable values (MPE-11.4% to -13.77%; MBE 0.49 to 0.96; RMSE 1.22 to 1.44; Skewness 0.90 to -0.15; Kurtosis 0.90 to -1.36 and Chi-square 6.87 to 8.50). The adequecy of empirical models was evaluated through comparing estimated and measured DNI for New Delhi and Thiruvanantpuram which coincides very closely. The study established an approach to estimate DNI using cloud cover data which may facilities CSP projects developers for site selection and decision making in the absence of ground data. [ABSTRACT FROM AUTHOR]

Reliable modelling of the solar potential of urban surfaces (i.e. roofs, facades and ground) in the built environment can contribute to boost the exploitation of solar energy. Empirical and quasi-physical models are combined into model chains for this scope. Among these models, the decomposition models are used to separate the direct from the diffuse solar irradiance. Such variables are rarely measured, thus making necessary their computation. Studies in literature pointed out that Yang4 is the best-performing decomposition model globally. However, in geographically limited applications, quasi-universal decomposition models like Yang4 and Engerer4 can be outperformed by local models (i.e. models parametrized with climate-specific data) such as Skartveit3 and Starke3. This makes necessary to perform local validation studies to verify the findings from worldwide validation studies. In this study, the four best-performing decomposition models are implemented in Python and experimentally validated against 1-min solar irradiance data (i.e. direct and diffuse irradiance) of Trondheim (Lat. 63°26′N, Norway). More than 1-year long data are considered. The study confirms that the Yang4 model performed the best for high-latitude applications, but Starke3 can achieve a level of performance close to Yang4. [ABSTRACT FROM AUTHOR]

Solar energy is a widely embraced renewable resource, characterized by its unpredictable, fluctuating, and stochastic nature. To mitigate risks and optimize asset utilization cost-effectively, precise analysis and forecasting of solar radiation can prove beneficial. This work aims to provide a hybrid model using machine learning to accurately predict solar Direct normal irradiance with the least amount of error. In this work, long short-term memory has been optimized using Particle swarm optimization, Grasshopper optimization algorithm, and Slime mold algorithm. SMA-LSTM, which has the best performance result compared to other developed models, is presented as the main method for this work. The data used is from June 1, 2022, to July 30, 2023. Many factors, such as the coefficient of determination, root mean square error, mean absolute percentage error, and mean absolute error have been used in presenting this work, and SMA-LSTM results with the lowest amount of R² has illustrated acceptable performance. [ABSTRACT FROM AUTHOR]

This article proposes a method to improve the efficiency of solar power plants by estimating and forecasting the spatial distribution of direct normal irradiance (DNI) using a sensor network and anemometer data. For this purpose, the proposed approach employs spatio-temporal kriging with an anisotropic spatio-temporal variogram that depends on wind speed to accurately estimate the distribution of DNI in real-time, making it useful for short-term forecast and nowcast of DNI. Finally, the method is validated using synthetic data from varying sky conditions, outperforming another state-of-the-art technique.

This research develops and validates new decomposition models for hourly direct Normal Irradiance (DNI) estimations for Southern African data. Localised models were developed using data collected from the Southern African Universities Radiometric Network (SAURAN). Clustered areas within Southern Africa were identified, and the developed cluster decomposition models highlighted the potential advantages of grouping data based on shared geographical and climatic attributes. This clustering approach could enhance decomposition model performance, particularly when local data are limited or when data are available from multiple nearby stations. Further, a regional Southern African decomposition model, which encompasses a wide spectrum of climatic regions and geographic locations, exhibited notable improvements over the baseline models despite occasional overestimation or underestimation. The results demonstrated improved DNI estimation accuracy compared to the baseline models across all testing and validation datasets. These outcomes suggest that utilising a localised model can significantly enhance DNI estimations for Southern Africa and potentially for developing similar models in diverse geographic regions worldwide. The overall metrics affirm the substantial advancement achieved with the regional model as an accurate decomposition model representing Southern Africa. Two stations were used as a validation study, as an application example where no localised model was available, and the cluster and regional models both outperformed the comparative decomposition models. This study focused on validating the model for hourly DNI in Southern Africa within a range of Kt-intervals from 0.175 to 0.875, and the range could be expanded and validated for future studies. Implementing accurate decomposition models in developing countries can accelerate the adoption of renewable energy sources, diminishing reliance on coal and fossil fuels.

In response to the challenges faced by solar power plants in Kupang, Indonesia -- particularly during the peak of the rainy season -- this study aims to enhance the WRF-Solar (Weather Research and Forecasting--Solar) numerical weather prediction model. Accurate short-term solar radiation forecasting is crucial for managing electricity supply disruptions and controlling operational costs in the City of Kupang and its surroundings. The unpredictable weather patterns -- characterized by intense cloud activity during these seasons -- pose significant challenges for reliable solar energy generation, thereby making precise weather modeling an essential tool for effective power plant management. Spanning 2020-2022, the research focused on the peak rainy seasons, utilizing a specifically configured WRF-Solar model to simulate solar radiation. Validation of the WRF-Solar model using observational data from the Automatic Solar Radiation Stations at the Kupang Climatological Station showed correlation values consistently above 0.50. However, the model exhibited a tendency to overestimate radiation intensity for GHI and DNI parameters (correlation 0.78 and 0.54) and underestimate for DHI (correlation 0.80), with RMSE values ranging from 47-320 W/m² and MBE values from -41.84 to 116.34 W/m². The model's accuracy was notably higher under clear conditions, with lower RMSE and MBE values but decreased significantly under overcast conditions, hence indicating sensitivity to cloud cover. This study highlights the WRF-Solar model's capability in capturing the diurnal pattern and hourly fluctuations of solar radiation while also underscoring the need for improved cloud representation to enhance accuracy under diverse climatic conditions. [ABSTRACT FROM AUTHOR]

This research develops and validates new decomposition models for hourly direct Normal Irradiance (DNI) estimations for Southern African data. Localised models were developed using data collected from the Southern African Universities Radiometric Network (SAURAN). Clustered areas within Southern Africa were identified, and the developed cluster decomposition models highlighted the potential advantages of grouping data based on shared geographical and climatic attributes. This clustering approach could enhance decomposition model performance, particularly when local data are limited or when data are available from multiple nearby stations. Further, a regional Southern African decomposition model, which encompasses a wide spectrum of climatic regions and geographic locations, exhibited notable improvements over the baseline models despite occasional overestimation or underestimation. The results demonstrated improved DNI estimation accuracy compared to the baseline models across all testing and validation datasets. These outcomes suggest that utilising a localised model can significantly enhance DNI estimations for Southern Africa and potentially for developing similar models in diverse geographic regions worldwide. The overall metrics affirm the substantial advancement achieved with the regional model as an accurate decomposition model representing Southern Africa. Two stations were used as a validation study, as an application example where no localised model was available, and the cluster and regional models both outperformed the comparative decomposition models. This study focused on validating the model for hourly DNI in Southern Africa within a range of K t -intervals from 0.175 to 0.875, and the range could be expanded and validated for future studies. Implementing accurate decomposition models in developing countries can accelerate the adoption of renewable energy sources, diminishing reliance on coal and fossil fuels. [ABSTRACT FROM AUTHOR]

Site adaptation has become a necessary step in resource assessment for ensuring the bankability of a renewable energy project. The process involves collecting short-term observation data to correct the long-term dataset available from the satellite-derived models, which could thus provide a more accurate estimate of the solar resource data. This study aims to enhance the site-adaptation of direct normal irradiance, as its correction remains notably challenging in comparison to global horizontal irradiance due to its larger error, which is often attributed to the complexity of cloud modeling. A new methodology for site-adaptation is proposed that exploits the use of a new indicator variable that describes the correctness of sky-condition classification by the clear-sky index. This variable has dual applications within the context of site adaptation: firstly, it is employed in the two-step binning procedure subsequent to the conventional clear-sky binning during preprocessing, and secondly, it serves as an additional input feature in machine-learning-based site adaptation. The results show that the former method can reduce the mean bias error to a mere 0.4%, while the latter is better for reducing large discrepancies as shown by the lower root mean squared error. [ABSTRACT FROM AUTHOR]

In this work, the cross-linear system, a recently developed concentrated solar power technology, is investigated for process heat application to mitigate the drawback of cosine loss at higher latitudes in current concentrated solar power technologies. The mathematical model for energy and exergy analysis has been developed and validated using computational analysis and experimental work previously performed. The simulation was conducted under the average direct normal irradiance of 756.47 W/m2 considered for 8 h (Sunny hours) for April 2017. The primary goal of the thermal investigation is to identify the optimal inlet parameters for the heat transfer fluid (HTF) while considering significant value of HTF outlet temperature, energy, and exergy efficiency of the receiver. The mathematical model is developed and simulated using Engineering Equation Solver software. Following an in-depth examination of the outcome, the optimum inlet HTF temperature and mass flow rate are established with noteworthy energy and exergy efficiency. The analysis demonstrates the utilisation of the cross-linear system for process heat applications at higher latitude locations (> 30°N) to circumvent the weakness of other existing concentrated solar power technologies due to the air's wide operating temperature range as HTF. The observed results show that this system provides hot air from 180 °C to 200 °C at a thermal efficiency of 45% −50% and exergy efficiency from 25% to 30% for the low inlet temperature of 60 °C to 100 °C. Even at higher latitudes (> 30°N), the cosine effect of 0.9 for 4 h duration is a unique advantage of this system. [ABSTRACT FROM AUTHOR]

One important aspect of solar radiation that has a direct impact on atmospheric processes, climatic conditions, and energy generation is direct normal irradiance. The integration of solar geometry, geographic location, and atmospheric characteristics is required for the prediction of Direct Normal Irradiance. Predicting Direct Normal Irradiance is essential for maximizing the efficiency of solar power plants in the field of renewable energy. Precise predictions facilitate the efficient assimilation of solar electricity into the electrical grid, augmenting energy production and maintaining system stability. This study uses the Genetic algorithm, Moth Flame Optimization, and Ant Lion Optimizer to optimize the Random Forest model. The basic approach for this study is provided as a novel hybrid method by combining Ant Lion Optimizer with Random Forest, which has the best performance outcome compared to other created models. The data used is from June 1, 2022, to July 30, 2023. In presenting this study, many aspects have been considered, including the coefficient of determination, root mean square error, mean absolute percentage error, and mean absolute error. The proposed model's findings with the highest amount of R-squared have shown satisfactory performance. [ABSTRACT FROM AUTHOR]

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 article Karout, 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

In this work, a model to estimate circumsolar normal irradiance (CSNI) for several half-opening angles under clear skies was developed. This approach used a look-up table to determine the model parameters and estimate CSNI for half-opening angles between 0.5° and 5°. To develop and validate the proposed model, data from five locations worldwide were used. It was found that the proposed model performs better at the locations under study than the models available in the literature, with relative mean bias error ranging from −13.94% to 0.70%. The impact of CSNI for these different half-opening angles on concentrating solar power (CSP) systems was also studied. It was found that neglecting CSNI could lead to up to a 7% difference between the direct normal irradiance (DNI) measured by a field pyrheliometer and the DNI that is captured by CSP systems. Additionally, a case study for parabolic trough concentrators was performed as a way to estimate the impact of higher circumsolar ratios (CSR) on the decrease of the intercept factor for these systems. It was also concluded that if parabolic trough designers aim to reduce the impact of CSNI variation on the intercept factor, then parabolic troughs with higher rim angles are preferred. [ABSTRACT FROM AUTHOR]

An accurate knowledge of the direct solar irradiance at normal incidence (DNI) is required to size solar energy systems, specially those using solar concentration technologies. In the absence of measurements, DNI can be estimated over an arbitrary site with a dedicated satellite model or by using satellite-derived global horizontal irradiance (GHI) and a phenomenological diffuse-direct separation model. This second procedure is error-prone, specially under partial cloudiness conditions and low solar elevation angles. A novel and simple semi-empirical satellite-based model to estimate DNI in moderate albedo regions using a physical clear sky model and a cloudiness index obtained from visible satellite imagery is proposed and assessed. An ergodic assumption in which the images are spatially averaged to better represent the hourly time basis is used. Over the target region (Southeastern South America, SESA), the new DNI satellite model outperforms the alternative strategy at the hourly level, with an average uncertainty of 20% (versus 25%) when compared with three measurement station's data sets located in center Argentina, southern Brazil and Uruguay. • A simple model to estimate hourly solar direct normal irradiation (DNI) is presented. • Its based on a precise clear sky model and a cloud index obtained from satellite imagery. • Spatial averaging of images over each site improves representation of hourly DNI. • Outperforms strategy of estimating DNI from direct-diffuse separation models and GHI. [ABSTRACT FROM AUTHOR]

• Spectrally resolved DNI was tested for an entire year in Beijing. • The spectra can be represented in five-dimensional data by an autoencoder. • Hierarchical clustering was adopted to obtain ten reference spectra in the case. • Novel local reference spectra can characterize solar resources throughout the year. • The superiority of the new reference spectra over ASTM AM1.5D was verified. The established reference spectrum (ASTM AM1.5) based on mid-latitudes is still the most frequently used standard in design and simulation in various fields worldwide, however, the solar spectrum varies with climate and time, which significantly affects the evaluations of solar energy utilization. This study proposes a novel reference spectrum analysis method to establish several local reference spectra, in this case, based on 147,374 spectra in Beijing, which can characterize solar resources throughout the year. The dataset consists of spectra of direct normal irradiance, and a deep learning autoencoder is adopted to extract the intrinsic features of each spectrum for clustering. 10 clusters are finally divided by agglomerative hierarchical clustering, and 10 local reference spectra were identified from each cluster by correlation analysis. The results show that the shape of new reference spectra shifts to the short wave as the irradiance increases, and the average photon energy increases from 1.069 eV to 1.456 eV. The new local reference spectra are compared with the standard spectrum AM1.5D to show the influence of shape variations, and it reveals 61% maximum differences among photovoltaic applications. The superiority verified new reference spectra could be further used to optimize local solar technologies design and guide photovoltaic material development. [ABSTRACT FROM AUTHOR]

The present paper deals with both the modeling and the dynamic control of a solar hybrid thermochemical reactor designed to produce syngas through the high-temperature steam gasification of biomass. First, a model of the reactor based on the thermodynamic equilibrium is presented. The Cantera toolbox is used. Then, a model-based predictive controller (MPC) is proposed with the aim of maintaining the reactor’s temperature at its nominal value, thus preserving the reactor’s stability. This is completed by adjusting the mirrors’ defocusing factor or burning a part of the biomass to compensate for variations of direct normal irradiance (DNI) round the clock. This controller is compared to a reference controller, which is defined as a combination of a rule-based controller and an adaptive proportional–integral–derivative (PID) controller with optimized gains. The robustness of the MPC controller to forecast errors is also studied by testing different DNI forecasts: perfect forecasts, smart persistence forecasts and image-based forecasts. Because of a high optimization time, the Cantera function is replaced with a 2D interpolation function. The results show that (1) the developed MPC controller outperforms the reference controller, (2) the integration of image-based DNI forecasts produces lower root mean squared error (RMSE) values, and (3) the optimization time is significantly reduced thanks to the proposed interpolation function.

Solar radiation influences many and diverse fields like energy generation, agriculture and building operation. Hence, simulation models in these fields often rely on precise information about solar radiation. Measurements are often restricted to global irradiance, whereby measurements of its single components, direct and diffuse irradiance, are sparse. However, information on both, the direct and diffuse irradiance, is necessary for simulation models to work reliably. In this study, solar separation models are developed using 10-min training data from two different locations in Austria. Direct horizontal irradiance is predicted via regressing the direct fraction using several objective functions. The models are first trained on a data set including data from both locations, and evaluated regarding root mean squared deviation (RMSD), mean bias deviation (MBD), and coefficient of determination (R2) on measured and estimated direct normal irradiance. The two best performing models are then selected for further analysis. This analysis comprises of an evaluation of the models per season, transferability of trained modes between two locations in Austria, a transferability and generalisability study conducted for four more locations in Central Europe, and a comparison with the trusted Engerer model. The solar separation model with polynomial terms up to order three and Ridge regularisation outperforms the second model based a logistic term in combination with mixed quadratic terms as well as the Engerer model.

• Totally 80 separation models of different time scales have been evaluated. • The Starke3 model has a better performance overall. • The geostationary satellite data has been used to analyze the model error. Diffuse irradiance and direct normal irradiance are two components that are critical to both energy and ecological systems; however, these are rarely available in most ground observations, which only provide the global horizontal irradiance (GHI), leaving the separation of them from the GHI an inevitable process. Although many separation models have been developed to provide solutions, a large proportion of these has been constructed based on site-specific datasets at different time resolutions, limiting their possibilities to be adopted to other sites without previously being extensively evaluated. In this study, we evaluated a total of 80 separation algorithms with specific attention to their time resolutions, based on the finest minute-scale observations from 13 irradiation sites across six Köppen-Geiger-Photovoltaic climate classification zones in East Asia-Pacific. We found that there was a dramatic difference in the performance of hourly-scale models and that the Starke3 model gave the best performance of the minute-scale models. Further residual analyses revealed that the clearness index, solar zenith angle, and atmospheric parameters played important roles. Since data from the geostationary Himawari-8 satellite are available to provide minute-scale atmospheric parameters, there is the possibility of reducing the residuals by incorporating the information with high temporal resolutions into separation models. The results of this study will provide a more comprehensive understanding of the utilization and improvement of separation models. [ABSTRACT FROM AUTHOR]

The present paper deals with both the modeling and the dynamic control of a solar hybrid thermochemical reactor designed to produce syngas through the high-temperature steam gasification of biomass. First, a model of the reactor based on the thermodynamic equilibrium is presented. The Cantera toolbox is used. Then, a model-based predictive controller (MPC) is proposed with the aim of maintaining the reactor's temperature at its nominal value, thus preserving the reactor's stability. This is completed by adjusting the mirrors' defocusing factor or burning a part of the biomass to compensate for variations of direct normal irradiance (DNI) round the clock. This controller is compared to a reference controller, which is defined as a combination of a rule-based controller and an adaptive proportional–integral–derivative (PID) controller with optimized gains. The robustness of the MPC controller to forecast errors is also studied by testing different DNI forecasts: perfect forecasts, smart persistence forecasts and image-based forecasts. Because of a high optimization time, the Cantera function is replaced with a 2D interpolation function. The results show that (1) the developed MPC controller outperforms the reference controller, (2) the integration of image-based DNI forecasts produces lower root mean squared error (RMSE) values, and (3) the optimization time is significantly reduced thanks to the proposed interpolation function. [ABSTRACT FROM AUTHOR]

• Accuracy of the 1-km MAIAC AOD and 2-km elevation-based MERRA-2 AOD currently used for NREL's NSRDB were evaluated. • The performance of MAIAC AOD is better than MERRA-2 AOD with smaller RMSE and MBE and higher R. • Simulated DNI using NREL's PSM with MAIAC AOD has higher accuracy than using MERRA-2 AOD. Solar irradiance, especially for direct normal irradiance (DNI), is sensitive to the atmospheric aerosols, which can strongly extinguish DNI over areas with elevated aerosol loadings. The National Solar Radiation Database (NSRDB), developed by the National Renewable Energy Laboratory (NREL), uses AOD from MERRA-2 reanalysis and is further downscaled from 0.5° to 2-km based on elevation. However, the elevation-based downscaling may not accurately represent AOD, especially over the areas with large AOD gradients. This study examined whether the 1-km MODIS MAIAC satellite-retrieved AOD product can better represent the AOD distribution and be used to improve solar irradiance assessment. We focused on areas with relatively high AOD over North America (California, and New York City, US, and Mexico City in Mexico, in particular). The evaluation of MAIAC AOD and MERRA-2 AOD against ground-truth AERONET AOD shows that MAIAC AOD exhibits smaller RMSE by about 0.05 and higher correlation coefficient by 0.1–0.6 than MERRA-2 AOD. In addition, MERRA-2 AOD shows larger negative bias. The simulated DNI using MAIAC AOD (DNIMAIAC) and using MERRA-2 AOD (DNINSRDB) were evaluated with DNI observations. The results show the performance of DNIMAIAC is better than that of DNINSRDB with smaller RMSE by 0.5 - 1.5%, smaller positive mean bias by 0.8 - 3.1 % and comparable correlation for the 3 sites analyzed. Overall, 1-km MAIAC AOD shows higher accuracy than 2-km elevation-based MERRA-2 AOD, leading to better performance of the simulated DNI using MAIAC AOD. Therefore, 1-km MAIAC AOD can be used to improve the accuracy of solar resource assessment. [ABSTRACT FROM AUTHOR]

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. • Short-term forecasting of direct normal irradiance (DNI) is obtained using a hybrid model, using both DNI measurements and ground-based sky imagery. • This hybrid model is compared to the smart persistence model and two machine learning models. • The results highlight the pertinence of combining knowledge-based models with data-driven models, and of integrating sky-imaging data. • Regardless of the forecast horizon, the hybrid model outperforms reference models, both in root mean squared error and in DNI ramp detection. [ABSTRACT FROM AUTHOR]

• Introducing MEMD-ALO-BiLSTM, a hybrid model for precise solar irradiance forecasting to boost hydrogen production. • Achieving a 0.99 coefficient of determination across seasons in Jiangsu Province, China, outperforming traditional models. • Optimizing photovoltaic systems and hydrogen production, supporting the transition to sustainable energy sources. • Validating the model's application in a residential energy system, demonstrating its practicality for renewable energy integration. • Enabling peak solar-powered hydrogen generation, marking a significant step towards reducing carbon emissions. This study focuses on an innovative hybrid machine-learning model for solar irradiance forecasting, targeting the integration of solar power into hydrogen production systems. Addressing the urgent need for sustainable energy transitions, the paper introduces the MEMD-ALO-BiLSTM model, designed to enhance solar irradiance prediction accuracy. This model uniquely combines Multivariate Empirical Mode Decomposition (MEMD), Ant Lion Optimizer (ALO), and Bidirectional Long Short-Term Memory (BiLSTM) techniques, setting a new benchmark in forecast precision across various seasonal datasets from Jiangsu Province, China. Demonstrating superior performance to traditional models, it achieves an exceptional coefficient of determination, averaging 0.99 for all seasons. Additionally, to prove the efficiency of the model three statistical tests were used, namely Wilcoxon, Friedman, and P-value. The research highlights the model's potential in optimizing photovoltaic systems and hydrogen production, thus contributing to carbon dioxide emission mitigation. Through comprehensive simulations of a residential system encompassing photovoltaic cells, compressors, and electrolyzers, the study underscores the practical feasibility and significant advancements the MEMD-ALO-BiLSTM model offers in the renewable energy sector, promoting a shift toward more reliable and efficient solar-powered hydrogen generation systems. Accordingly, the day-ahead values of photovoltaic-generated power and hydrogen production through the electrolyzer reached peak values at 1:00PM with approximately 75 kW and 1.4 kg, respectively. [Display omitted] [ABSTRACT FROM AUTHOR]

Evaluating, mapping, and monitoring high-quality Direct Normal Irradiance (DNI) is crucial for the design, financing, and operation of solar power plants, especially those utilizing concentrating technologies. In China, limited number of first-class meteorological stations provide DNI observations. To bridge this gap, this study has reconstructed a comprehensive 41-year (1982–2022) daily DNI dataset for China (CHDNI). This dataset integrated CMA ground-based observations with ERA5 and MERRA-2 reanalysis data, employing a spatial resolution of 10 km. The data was processed using the REST2 radiation transfer model combined with Stacking ensemble machine learning. Validation with ground-based measurements indicated that the Stacking model exhibited high and consistent performance. This was evidenced by its sample-based cross-validation results, showing a correlation coefficient (R) of 0.92, a root-mean-square error (RMSE) of 32.04 W/m2, a mean absolute error (MAE) of 23.68 W/m2, and a global performance indicator of 3.86. Among the four analyzed DNI products (CERES, ERA5, SWGDN, and CHDNI), CHDNI demonstrated the closet alignment with ground observations, evidenced by the least deviation (MAE = 21.67 W/m2 and RMSE = 31.00 W/m2) and the highest R value (0.92). Aerosol optical depth and cloud cover emerged as the primary factors influencing the quality and accuracy of DNI products under clear-sky and total-sky conditions. The spatial distribution of DNI across China is complex, showcasing DNI values ranging from 25.82 W/m2 to 194.22 W/m2, with an average value of 98.08 W/m2 over 41-year. This dataset is a valuable resource for analyzing regional climate change, photovoltaic applications, and solar energy resources assessment. • A hybrid algorithm combining radiative transfer model and Stacking machine learning was proposed to precisely estimate DNI. • A high-resolution (10 km), continuous gridded DNI dataset over China from 1982 to 2022 was reconstructed. • An increase of 0.5 in AOD results in maximum RMSE and MAE of DNI products increasing by 48.03 and 32.23 W/m2. • A 0.2 increase in cloudiness alters RMSE and MAE of DNI products by up to −5.30 and −4.95 W/m2. [ABSTRACT FROM AUTHOR]

In this work, a model to estimate circumsolar normal irradiance (CSNI) for several half-opening angles under clear skies was developed. This approach used a look-up table to determine the model parameters and estimate CSNI for half-opening angles between 0.5° and 5°. To develop and validate the proposed model, data from five locations worldwide were used. It was found that the proposed model performs better at the locations under study than the models available in the literature, with relative mean bias error ranging from −13.94% to 0.70%. The impact of CSNI for these different half-opening angles on concentrating solar power (CSP) systems was also studied. It was found that neglecting CSNI could lead to up to a 7% difference between the direct normal irradiance (DNI) measured by a field pyrheliometer and the DNI that is captured by CSP systems. Additionally, a case study for parabolic trough concentrators was performed as a way to estimate the impact of higher circumsolar ratios (CSR) on the decrease of the intercept factor for these systems. It was also concluded that if parabolic trough designers aim to reduce the impact of CSNI variation on the intercept factor, then parabolic troughs with higher rim angles are preferred.

This article proposes a method to improve the efficiency of solar power plants by estimating and forecasting the spatial distribution of direct normal irradiance (DNI) using a sensor network and anemometer data. For this purpose, the proposed approach employs spatio-temporal kriging with an anisotropic spatio-temporal variogram that depends on wind speed to accurately estimate the distribution of DNI in real-time, making it useful for short-term forecast and nowcast of DNI. Finally, the method is validated using synthetic data from varying sky conditions, outperforming another state-of-the-art technique., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Eduardo Fernandez Camacho reports was provided by 10.13039/501100000781European Research Council (grant agreement No 789051). Jose Maria Maestre Torreblanca reports financial support was provided by Spain Ministry of Science and Innovation (Grant no. PID2023-152876OB-I00). If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

The rapid increase in solar photovoltaic (PV) integration into electricity networks introduces technical challenges due to varying PV outputs. Rapid ramp events due to cloud movements are of particular concern for the operation of remote islanded microgrids (IMGs) with high solar PV penetration. PV systems and optionally controllable distributed energy resources (DERs) in IMGs can be operated in an optimised way based on nowcasting (forecasting up to 60 min ahead). This study aims to evaluate the performance under Perth, Western Australian conditions, of an all-sky imager (ASI)-based nowcasting system, installed at Murdoch University in Perth, Western Australia (WA). Nowcast direct normal irradiance (DNI) and global horizontal irradiance (GHI) are inputted into a 5 kWp solar PV system with a direct current (DC) power rating/alternating current (AC) power rating ratio of 1.0. A newly developed classification method provided a simplified irradiance variability classification. The obtained nowcasting system evaluation results show that the nowcasting system's accuracy decreases with an increase in lead time (LT). Additionally, the nowcasting system's accuracy is higher when the weather is either mostly clear (with a recorded LT15 mean absolute deviation (MAD) of 0.38 kW) or overcast (with a recorded LT15 MAD of 0.19 kW) than when the weather is intermittently cloudy with varying cloud conditions (with a recorded LT15 MAD of 0.44 kW). With lower errors observed in lower LTs, overall, it might be possible to integrate the nowcasting system into the design of IMG controllers. The overall performance of the nowcasting system at Murdoch University was as expected as it is comparable to the previous evaluations in five other different sites, namely, PSA, La Africana, Evora, Oldenburg, and Julich. [ABSTRACT FROM AUTHOR]

The evaluation of solar radiation is essential for large-scale solar energy systems, as assessing economic feasibility early on depends on accurate solar radiation data. Accurate sensors are needed to characterize the solar resource. Due to a scarcity of solar radiation data, numerical models are commonly used to estimate solar radiation components using meteorological variables that are simple or cheap to measure. In recent years, the use of machine learning (ML) algorithms has gained significant popularity in the estimation of solar radiation components. In this study it is proposed a post-processing approach using the separation model outcomes as input variables to estimate the diffuse fraction. Three ML models are employed (XGBoost, Random Forest, and Multilayer Perceptron) to boost the accuracy in terms of three statistical indicators: nRMSE, nMBE, and R 2. The employed technique takes a distinctive approach by using reference stations to train the machine learning models and, afterward, make the assessment at the site under study. The results show an improvement in terms of precision of individual separation model outcomes. Thus, the proposed methodology may serve as a reliable approach for estimating solar radiation components in cases where historical data for a specific place of interest is not accessible. • Evaluation of three ML models to enhance the estimation of solar irradiance. • The models are trained with reference stations for every climate zone • Assessment of the models with the stations classified in the same climate zone. • Comparison against the individuals separation models. [ABSTRACT FROM AUTHOR]

Since the fossil reserves are depleting day by day, the trend of modern energy sector is going towards renewable energy. The demand of solar power plants is therefore at the peak nowadays across the globe. However, the construction of these plants is extremely dependent on feasibility study to estimate the real solar potential before installing it in any region. To evaluate the solar energy potential of Peshawar region in Pakistan, Ground-based global horizontal irradiance (GHI) and direct normal irradiance (DNI) were compared with satellite-based model SUNY. Ground measurements were done at the University of Engineering and Technology Peshawar (UET Peshawar) with the help of pyranometer and shadowband irradiometer. Comparison of the data showed that there was a maximum difference of 42.90% in ground and satellite-based GHI in the month of December. Minimum difference in GHI was found for the month of March that was −3.83%. Moreover, ground-based GHI was overestimated in the month of February, March, and April, while in rest of the months, satellite values of GHI exceeded the ground measurements. Similarly, maximum difference of 55.86% was found in the month of November between ground and satellite-based DNI while minimum difference of −3.34% was seen in DNI in the month of March between the two data. Furthermore, satellite-based DNI was underestimated in the months of February, March, and April while in rest of the months it was overestimated compared to ground measurements. In addition to this, correlation of ground and satellite-based GHI and DNI showed R2 value of 0.8852 and 0.4139, respectively. The results of this study revealed that the difference between ground measurements and satellite values was considerable and hence real time measurements are necessary to properly estimate solar energy resource in the country. [ABSTRACT FROM AUTHOR]

• In less-polluted conditions, irradiance is sensitive to cloud parameters in CLD3. • In severe wildfire conditions, elevated sensitivity to aerosol parameters in TE14. • Parameter induced uncertainties vary with cloud fraction and AOD. We investigate the parametric sensitivity of solar irradiance to a set of parameters within the sub-grid cloud scheme (CLD3) and an upgraded aerosol-aware Thompson-Eidhammer scheme (TE14) in an upcoming enhanced version of the Weather Research and Forecasting-Solar model. We conduct ensemble simulations over the Southern Great Plains and Hanford, California, focusing on the parametric sensitivity under cloudy conditions with different aerosol loading. We adopt the Quasi-Monte Carlo sampling approach to explore the high-dimensional parameter space and apply the generalized linear model to quantify the relative contribution of individual parameters to the total variance. We find that CLD3 parameters related to entrainment and the cloud condensation threshold contribute to most of the variance in the ensemble simulations under less-polluted conditions. Larger entrainment and a higher condensation threshold produce more solar irradiance via decreasing cloud fraction and cloud optical depth. As the sensitivities of cloud fraction and cloud optical properties to perturbed parameters vary with cloud cover, the relative contributions of parameters to irradiance variance also change with cloud cover. As a contrast, under the heavily-polluted wildfire conditions, the model sensitivities to the parameters within the enhanced TE14 scheme become pronounced, especially those related to water-friendly aerosol emission rate and the modal radius of black carbon. The variations of those aerosol parameters cause significant changes in simulated irradiance, especially over the clear sky, suggesting the importance of accurate representation of emission source for aerosols and their precursors. [ABSTRACT FROM AUTHOR]

• Direct normal irradiance (DNI) is computed by a physics-based solution of the gross solar radiation in the circumsolar region. • The new DNI model is coupled with a numerical weather prediction model to forecast day-ahead DNI over the contiguous United States. • The significant bias in the conventional forecasting models has been remarkably reduced. Direct normal irradiance (DNI) is often interpreted differently in ground measurements and forecasts using numerical weather prediction (NWP) models, leading to substantial bias in DNI forecasting especially under cloudy-sky conditions. To mitigate the bias, we use the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI) to conduct physics-based simulations of solar radiation in the circumsolar region. The DNI is quantified using the sum of the radiation along the sun direction and the scattered radiation within the circumsolar region. FARMS-DNI is coupled with the Weather Research and Forecasting model with solar extensions (WRF-Solar) to predict day-ahead DNI in 9-km pixels over an extended domain covering the contiguous United States. The DNI forecasts for the entire year of 2018 are validated using surface-based observations at the Atmospheric Radiation Measurement (ARM) and Surface Radiation Budget Network (SURFRAD) sites and a satellite-based solar radiation product developed by the procedure of the National Solar Radiation Data Base (NSRDB). The results show that the WRF-Solar coupled with the conventional Beer-Bouguer-Lambert law underestimes the overall DNI forecasts by 30%-60%, which is a joint effect of the uncertainties in computing the circumsolar radiation and forecasting cloud properties. This bias is significantly reduced by using FARMS-DNI. [ABSTRACT FROM AUTHOR]

The focus of this study is to develop a highly accurate formulation to estimate the day number (DN), solar declination angle (SOLDEC), solar altitude angle (SOLALT) and also to predict the diffuse horizontal irradiance (DHI), direct normal irradiance (DNI) and global horizontal irradiance (GHI) for any location around the world at any time of the day for both short term and long term periods. Regression analysis is done using continuous 12 years of satellite measured historical solar irradiance, weather and solar angle data in the temporal resolution of 10 min for 12 cities around the world such as Kuala Lumpur, Auckland, Tokyo, Riyadh, London, Accra, Antananarivo, Brasilia, Lima, Quito, Ottawa and Honolulu. The models generated through the regression analysis perform better than existing models in predicting the solar irradiance, hence, these models are efficient and reliable for universal global application. [ABSTRACT FROM AUTHOR]

• Impact varied with time of occurrence, strength of obscuration and sky cover. • Surface radiation components are sensitive to the fraction of non-obscured area. • Diffuse fraction is spectrally insensitive at peak obscuration and under clouds. • Asymmetry in the rates of decrement/increment in irradiance. • PV energy generation followed linear growth and reduced by 37%. We investigate the impact of annular solar eclipse (26 December 2019) on the surface radiation and photovoltaic (PV) energy generation observed at Gadanki (13.48° N, 79.18° E), located in southern peninsular India. Spectral and broadband irradiances are found to be sensitive to the solar obscuration, and follow the curvature resembling the fraction of non-obscured area of the Sun during the eclipse. Rapid changes are noticed in the spectral and broadband irradiances during the decreasing and increasing phases of solar obscuration. The diffuse fraction exhibits weak spectral dependence during the peak solar obscuration and under the influence of clouds. Although the deviation in the diffuse horizontal irradiance (DHI) at different wavelengths is highly sensitive, the deviations in the global horizontal irradiance (GHI) and direct normal irradiance (DNI) are found to be comparable among different wavelengths during the eclipse period. At peak obscuration, the reduction in both GHI and DNI shows an increasing trend from 414 nm to 671.2 nm. On the contrary, DHI decreases from 495.9 nm to 869 nm. Also, the deviation in broadband irradiances range between 72% and 98% with differing magnitudes of reduction at the peak obscuration. Apart from this, it is observed that the time of occurrence, strength of solar obscuration and prevailing sky conditions exhibit varying impact on the surface radiation components. The PV energy generated on the eclipse day is found to be reduced by 37% in comparison to that on a clear-sky day. [ABSTRACT FROM AUTHOR]

One of the crucial challenges faced by industry has been finding approaches that meet its increasing energy demand and decreasing reliance on non-renewable sources. Ecuadorian policies promote the use of renewable sources of energy; nevertheless, there is limited research on concentrated solar energy in the country. Therefore, this review article presents an overview on previous research on the description of linear Fresnel collectors for solar heat for industrial processes, promising Ecuadorian industrial branches for its application, and the solar resource available for this purpose in Ecuador. As a result of existing literature analysis, the manufacturing industry may be a key sector for the application of this technology, which could reduce the use of conventional energy sources, especially in the food industry located in the Andean region. The outcomes will contribute to future thorough research on the topic.

Boosted by a strong solar power market, the electricity grid is exposed to risk under an increasing share of fluctuant solar power. To increase the stability of the electricity grid, an accurate solar power forecast is needed to evaluate such fluctuations. In terms of forecast, solar irradiance is the key factor of solar power generation, which is affected by atmospheric conditions, including surface meteorological variables and column integrated variables. These variables involve multiple numerical time-series and images. However, few studies have focused on the processing method of multiple data types in an inter-hour direct normal irradiance (DNI) forecast. In this study, a framework for predicting the DNI for a 10-min time horizon was developed, which included the nondimensionalization of multiple data types and time-series, development of a forecast model, and transformation of the outputs. Several atmospheric variables were considered in the forecast framework, including the historical DNI, wind speed and direction, relative humidity time-series, and ground-based cloud images. Experiments were conducted to evaluate the performance of the forecast framework. The experimental results demonstrate that the proposed method performs well with a normalized mean bias error of 0.41% and a normalized root mean square error (nRMSE) of 20.53%, and outperforms the persistent model with an improvement of 34% in the nRMSE.