Diffuse Horizontal Irradiance Research Articles

A CERES-based dataset of hourly DNI, DHI and global tilted irradiance (GTI) on equatorward tilted surfaces: Derivation and comparison with the ground-based BSRN data

The NASA CERES SYN1deg(Ed4.1) satellite-based products include hourly global horizontal irradiance (GHI), diffuse horizontal irradiance (DHI) and direct horizontal irradiance (DirHI) as well as hourly solar zenith angle (SZA), and the direct normal irradiance (DNI) can thus be calculated by dividing the DirHI by cos(SZA). The spatial resolution of the dataset is 1° latitude by 1° longitude, and the time span is from March 2000 to near present (October 2023 as of this writing). While the GHI of the dataset agrees well with the ground-based Baseline Surface Radiation Network (BSRN) data, the DHI and DirHI, and thus DNI, show appreciable biases against the BSRN data. Nevertheless, the uncertainty of the DHI and DNI, as exhibited in the standard deviations of their differences from the BSRN data, are smaller than results from the DirIndex model, a global-to-beam model, applied to the CERES data, albeit the model is one of the two best out of 140 models according to a study. This means that the original CERES hourly DNI, though biased, renders the spatiotemporal variability better than does the DirIndex model. For this reason, we decided to perform a bias correction on the hourly DHI and DNI. With the corrected hourly DHI and DNI, we are able to use the isotropic model to calculate the global tilted irradiance (GTI) and the global tracker irradiance (GTrI) on an hourly basis. The BSRN DHI and DNI, on the other hand, are available on near-instantaneous time scales (1-, 2-, 3, or 5-minute intervals) and thus are more conducive to the isotropic model, and for the first time, we use the BSRN data to validate the GTI and GTrI, as opposed to GHI and DHI that have previously been used for validation purpose. For comparison and validation, we also use the monthly-mean-based LJCR method, an independent method as used by RETScreen®, which requires monthly mean GHI and DHI, to directly calculate the monthly mean DNI, GTI and, to extend the model, GTrI, and the results agree well with the BSRN data, which proves the LJCR method itself and corroborates the corrected DHI and DNI as well as the derived GTI and GTrI. The monthly mean DHI and DNI from the Whitlock Method, also monthly-mean-based, are evaluated as well, and we find that minor modification can dramatically improve the method.

Predicting global horizontal irradiance of north central region of India via machine learning regressor algorithms

The ability to predict global horizontal irradiance (GHI) in the wake of changing weather conditions is becoming increasingly important in driving economic growth in the renewable energy industry. The choice of relevant meteorological variables for predicting GHI is crucial because it influences the prediction accuracy due to various geographical conditions. To fulfill this aim, this proposed study identifies a subset of the relevant input variables for the prediction of GHI by applying two methods Feature Combination (FC) and Feature Selection (FS). The results reveal that the most significant input variables for predicting GHI are Solar Zenith Angle, Dew Point, Diffuse Horizontal irradiance, Direct Normal Irradiance, and Wind speed obtained by the FS method. The predictive performance of the selected features is evaluated by feeding them into six different types of Machine Learning (ML) regressor algorithms such as Multiple Linear regressor (MLR), Decision Tree (DT), Random Forest (RF), Gradient Boost (GB), Light Gradient Boost Machine (LGBM) and Extra Tree (ET). The performances of models are evaluated by using statistical measures such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). ET regressor gives the best prediction performance among all the six models. It is observed that the percentage reduction in error for the FS method as compared to the FC method in terms of the statistical indicators MAE, RMSE, and MAPE are 8.5%, 7.72%, and 25.84% respectively. It shows that the FS method gives better predictive performance than the FC method for the estimation of GHI.

Solar and Wind Data Recognition: Fourier Regression for Robust Recovery

Accurate prediction of renewable energy output is essential for integrating sustainable energy sources into the grid, facilitating a transition towards a more resilient energy infrastructure. Novel applications of machine learning and artificial intelligence are being leveraged to enhance forecasting methodologies, enabling more accurate predictions and optimized decision-making capabilities. Integrating these novel paradigms improves forecasting accuracy, fostering a more efficient and reliable energy grid. These advancements allow better demand management, optimize resource allocation, and improve robustness to potential disruptions. The data collected from solar intensity and wind speed is often recorded through sensor-equipped instruments, which may encounter intermittent or permanent faults. Hence, this paper proposes a novel Fourier network regression model to process solar irradiance and wind speed data. The proposed approach enables accurate prediction of the underlying smooth components, facilitating effective reconstruction of missing data and enhancing the overall forecasting performance. The present study focuses on Midland, Texas, as a case study to assess direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and wind speed. Remarkably, the model exhibits a correlation of 1 with a minimal RMSE (root mean square error) of 0.0007555. This study leverages Fourier analysis for renewable energy applications, with the aim of establishing a methodology that can be applied to a novel geographic context.

Composition of feature selection techniques for improving the global horizontal irradiance estimation via machine learning models

The rise in photovoltaic capacity installed globally and the sporadic nature of solar resources underline the significance of solar radiation forecasting for grid integration. The choice of relevant climatic variables for estimating global horizontal irradiance (GHI) is crucial because it influences estimation accuracy. To keep this in mind, different feature selection techniques such as filter, wrapper, and embedded are computed and compared to identify a pertinent input variable for the prediction of GHI. In order to reduce the dimensionality of the input features, it is pertinent to remove unnecessary variables using feature selection techniques. This comprehensive study explores the use of tree-based ensemble machine learning (ML) models in the modeling of global solar radiation. Eighteen statistical and ML regressor algorithms, along with ten feature selection methods are evaluated based on statistical comparisons of their computational effectiveness and estimation errors. The lowest mean absolute error (MAE) and root mean squared error (RMSE) of 0.9938 and 2.5327 achieved by the extra-tree regressor algorithm reveals that it outperforms the other models on the test dataset. The integration of feature selection techniques improves predictive capabilities, reducing MAE and RMSE by 13.15% and 5.24%, respectively, for the Mutual Information method. This study also identifies influential features critical to accurate GHI estimation, such as solar zenith angle, diffuse horizontal irradiance, and direct normal irradiance, which provide valuable insights into the dynamics governing solar radiation estimation.

Influences of Cloud Microphysics on the Components of Solar Irradiance in the WRF-Solar Model

An accurate forecast of Global Horizontal solar Irradiance (GHI) and Direct Normal Irradiance (DNI) in cloudy conditions remains a major challenge in the solar energy industry. This study focuses on the impact of cloud microphysics on GHI and its partition into DNI and Diffuse Horizontal Irradiance (DHI) using the Weather Research and Forecasting model specifically designed for solar radiation applications (WRF-Solar) and seven microphysical schemes. Three stratocumulus (Sc) and five shallow cumulus (Cu) cases are simulated and evaluated against measurements at the US Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility, Southern Great Plains (SGP) site. Results show that different microphysical schemes lead to spreads in simulated solar irradiance components up to 75% and 350% from their ensemble means in the Cu and Sc cases, respectively. The Cu cases have smaller microphysical sensitivity due to a limited cloud fraction and smaller domain-averaged cloud water mixing ratio compared to Sc cases. Cloud properties also influence the partition of GHI into DNI and DHI, and the model simulates better GHI than DNI and DHI due to a non-physical error compensation between DNI and DHI. The microphysical schemes that produce more accurate liquid water paths and effective radii of cloud droplets have a better overall performance.

An Assessment of Solar Micro-Grid System in the Islands of Bangladesh for Sustainable Energy Access

The isolated islands in the northern Bay of Bengal face difficulties accessing electricity from the central grid and use fossil fuel-based generators, which causes health risks, environmental damage, and high expenses. So, this study aims to replace these fossil fuel-based power sources with Solar Photovoltaic (SPV) microgrids to provide continuous power to remote islands and contribute to reducing emissions. A specific location on the island of Manpura is selected for the SPV plant installation, considering land availability and vulnerability to erosion. Solar power density and other technical parameters such as Direct Normal Irradiation, Global Horizontal Irradiation, Diffuse Horizontal Irradiation, Global Tilted Irradiation, the optimum tilt angle of Photovoltaic (PV) modules, air temperature, and terrain elevation are analyzed using some prominent online analytical tools named “Global Solar Atlas (GSA)”, “Photovoltaic Geographical Information System (PVGIS)” etc. Based on these datasets, a 10kW ground-mounted PV system using monocrystalline silicon solar panels is designed for off-grid operation. The developed system can generate 14.808 MWh of energy per year. This design’s environmental and social impacts are critically analyzed considering the Sustainable Development Goals (SDGs). Compared with conventional energy sources, the results show that the 10kW microgrid SPV system can reduce CO2 emissions by 284 tons. Financial analysis shows the recovery period of the investment based on electricity production at around 8.8 years, making solar PV microgrids a viable option for remote areas like Manpura Island.

Development of typical meteorological years based on quality control of datasets in Indonesia

This study aims to demonstrate the comprehensive development of typical meteorological years (TMYs) under relatively limited observational data. The distribution of missing hourly observational data of the 2011–2020 period at all sites was examined. This paper proposes a quality control method for filling the gaps in the missing hourly observational data using bias-corrected ERA5 reanalysis data in the process of developing TMYs. Initially, the temperature bias distribution from −4.5 °C to 2.7 °C was reduced to a range of −0.014 °C to 0.005 °C. The relative humidity bias distribution was −6 % to 10 %, and was reduced to −0.32 % to 0.07 %. The bias distribution of wind speeds ranging from −4 m/s to 2 m/s was reduced to −0.02 m/s to 0.35 m/s. The Sandia method with a modified weighting of Finkelstein-Shaffer (FS) statistics was applied to eight climate elements, namely, global horizontal irradiance, direct normal irradiance, diffuse horizontal irradiance, temperature, precipitation, wind speed, relative humidity, and dew point temperature to generate TMYs at 106 sites across eight climate zones in Indonesia. The verification results showed that the average correlation and RMSE between TMYs and their long-term averages were 0.96 and 75 w/m2 for global horizontal radiation, respectively, while those for temperature were 0.86 and 1.3 °C, respectively.

NTNU-SINTEF SolarNet: A solar irradiation monitoring network at high latitudes

This study presents a monitoring network for solar irradiation at high latitudes, called NTNU-SINTEF SolarNet. The network collects, with a time resolution ranging from seconds to hours, solar irradiance data, e.g. global horizontal irradiation, diffuse horizontal irradiation, direct normal irradiation, global tilted irradiation, solar energy generation, which are required in solar irradiation modelling in built environments. The network will be used for specific applications, such as (i) anomalies detection, (ii) influences of ground albedo, and (iii) ageing/degradation of solar modules, that are described in this paper. Some characteristics that make the NTNU-SINTEF SolarNet relevant for solar energy research at high latitudes are identified: short distances among the sensors, the ease of data accessibility, the use of the same sensor typologies, and different solar module technologies. The research holds the potential to boost the solar energy digitalization, impacting on several aspects such as predictive and adaptive control strategies for energy management, design of renewable energy system, multi-scale optimization and efficient exploitation of solar energy.

Regime-dependent 1-min irradiance separation model with climatology clustering

Since directly measuring beam and diffuse irradiance is not feasible on many occasions, one often has to resort to estimating the beam and diffuse irradiance components from the global irradiance, which is known as separation modeling. Separation modeling is essentially a nonlinear regression problem, with the clearness index being the main input and the diffuse fraction being the output. Hundreds of separation models with various complexities have been proposed, among which the Yang4 model was recently validated using worldwide data as the quasi-universal choice for 1-min data. In this work, Yang4 is further improved by regime-dependent fitting, i.e., fitting a separate set of model coefficients for each climatological regime. Different regimes are determined through clustering of cloud cover frequency, aerosol optical depth, and surface albedo climatology maps. The new Yang5 model is able to outperform its predecessor at the 126 stations tested, covering a wide range of climate types. Overall, the normalized root mean square errors for beam normal irradiance (BNI) and diffuse horizontal irradiance (DHI) of Yang5 are 17.55% and 32.92% on average, as compared to 19.13% and 34.94% for the next best model, namely, Yang4. Furthermore, through conducting pairwise Diebold–Mariano tests, Yang5 is shown superior to Yang4 at 110/126 sites for BNI prediction and 93/126 for DHI.

Energy Yield Prediction of Bifacial Perovskite/Silicon Tandem Photovoltaic Modules

Bifacial perovskite (PVK)/crystalline silicon (c‐Si) tandem photovoltaic (PV) modules provide an effective strategy to further improve the efficiency and energy yield (EY) of c‐Si PV modules. In this work, the energy outputs of bifacial tandem PV modules under outdoor conditions are analyzed by combining optical modeling, one‐diode equivalent circuit model, module tilt angle, and meteorological data, and a detailed comparative study with bifacial silicon heterojunction (SHJ) PV modules is also performed. The bifacial PVK/c‐Si tandem modules exhibit higher EY in locations with strong direct normal irradiance regardless of the ground type with any albedo, while bifacial SHJ modules exhibit similar or even higher EY than tandem modules in locations with strong diffuse horizontal irradiance (DHI) such as Chengdu. Considering factors such as EY and levelized cost of electricity, Eg_PVK in the range of 1.58–1.62 eV is suitable for most application scenarios, while the deployment of bifacial SHJ PV modules is preferred in areas dominated by DHI, as represented by Chengdu. Herein, important implications for policy making in determining the cost‐effective type of PV modules are provided to generate renewable electricity.

Minute-Scale Models for the Diffuse Fraction of Global Solar Radiation Balanced between Accuracy and Accessibility

The separation models are tools used in solar engineering to estimate direct normal (DNI) and diffuse horizontal (DHI) solar irradiances from measurements of global solar irradiance (GHI). This paper proposes two empirical separation models that stand out owing to their simple mathematical formulation: a rational polynomial equation. Validation of the new models was carried out against data from 36 locations, covering the four major climatic zones. Five current top minute-scale separation models were considered references. The tests were performed on the final products of the estimation: DNI and DHI. The first model (M1) operates with eight predictors (evaluated from GHI post-processed measurements and clear-sky counterpart estimates) and constantly outperforms the already established models. The second model (M2) operates with three predictors based only on GHI measurements, which gives it a high degree of accessibility. Based on a statistical linear ranking method according to the models’ performance at every station, M1 leads the hierarchy, ranking first in both DNI and DHI estimation. The high accessibility of the M2 does not compromise accuracy; it is proving to be a real competitor in the race with the best-performing current models.

Horizontal and vertical migration of cyanobacterial blooms in two eutrophic lakes observed from the GOCI satellite

Under the variations of natural conditions (temperature, wind speed, light, et al.) and self-regulation of buoyancy, cyanobacterial blooms can change rapidly in a short time. The Geostationary Ocean Color Imager (GOCI) can provide hourly monitoring of the dynamics of algal blooms (eight times per day), and has potential in observing the horizontal and vertical movement of cyanobacterial blooms. Based on the fractional floating algae cover (FAC), the diurnal dynamics and migration of floating algal blooms were evaluated, and the horizontal and vertical migration speed of phytoplankton was estimated from the proposed algorithm in two eutrophic lakes, Lake Taihu and Lake Chaohu in China. The locations, number, and area of algal bloom patches showed the hotspots and horizontal movement of bloom patches. The spatial and seasonal variations of the vertical velocities indicated that both the rising and sinking speed were higher in summer and autumn than those in spring and winter. The factors affecting diurnal horizontal and vertical migrations of phytoplankton were analyzed. Diffuse horizontal irradiance (DHI), direct normal irradiance (DNI), and temperature had significant positive relationships with FAC in the morning. Wind speed contributed 18.3 and 15.1% to the horizontal movement speed in Lake Taihu and Lake Chaohu, respectively. The rising speed was more related to DNI and DHI in Lake Taihu and Lake Chaohu with contribution of 18.1 and 16.6%. The horizontal and vertical movement of algae provide important information for understanding phytoplankton dynamics and the prediction and warning of algal blooms in lake management.

Data-Driven Hyperparameter Optimized Extreme Gradient Boosting Machine Learning Model for Solar Radiation Forecasting

The uncertainty of the non-conventional sources especially solar energy caused due to spatio-temporal factors like temperature, pressure, relative humidity etc. is continuously disrupting the productivity and reliability of an integrated power system which motivates the researcher or energy industry for strategic forecasting solutions to enhance the proper scheduling and control of solar generation power plants. Several studies have been carried out; but still the objective of achieving accurate forecasting dependent on the spatio-temporal features is not achieved. To address this critical forecasting issue in this research article a hyper parametric tuning of the Extreme Gradient Boosting (XGB) machine learning model has been carried out using two met heuristic algorithms: Moth Flame Optimization (MFO) and Grey Wolf Optimization (GWO). The dataset comprises five years of metrological attributes collected from the National Renewable Energy Laboratory (NREL) for analysis. The validation of the proposed model has been done based on the five statistical errors: Max Error (ME), Mean Absolute Error (MAE), Coefficient of Determination (R^2), Mean Square Error (MSE) and Root Mean Square Error (RMSE). The regressive assessment of all three models has confirmed that the XGB-MFO model outperformed the others as showing the highest R^2 score of 0.9337, 0.9011, 0.8744 and lowest RMSE values of 76.29 Wcm^{-2}, 41.90 Wm^{-2} and 95.94Wm^{-2} for Global Horizontal Irradiance (GHI), Diffuse Horizontal Irradiance (DHI) and Direct Normal Irradiance (DNI) respectively which ensures the proposed model implementation for the prediction and production of solar power.

Feasibility and performance analysis of bifacial module under extremely hot and dry climate

Solar panels with a bi-facial design provide energy from both sides; thus, it would be interesting to examine the viability of such modules in an extremely hot and dry city like Ahmedabad, Gujarat, India. This study assesses the temperature, efficiency, and bifacial gain of PV bifacial modules as a function of solar irradiation, ambient temperature, wind speed, and additional parameters. The global horizontal irradiance has been used in this study to calculate Direct Normal Irradiance (DNI), Diffuse Horizontal Irradiance (DHI) and Front and Rear Irradiance. The results show that an optimal tilt angle of 43.5° provides the best efficiency of 24% at ambient temperature. For a constant value of tilt angle, the bi-facial gain of the module increases linearly with albedo factor. It has also been discovered that continuing to raise the elevation beyond 1 meter will eventually diminish the bifacial gain because the reflected and diffuse irradiance will become less intense on the rear side of the bifacial module.

Modeling Horizontal Ultraviolet Irradiance for All Sky Conditions by Using Artificial Neural Networks and Regression Models

In the present study, different models constructed with meteorological variables are proposed for the determination of horizontal ultraviolet irradiance (IUV), on the basis of data collected at Burgos (Spain) during an experimental campaign between March 2020 and May 2022. The aim is to explore the effectiveness of a range of variables for modelling horizontal ultraviolet irradiance through a comparison of supervised artificial neural network (ANN) and regression model results. A preliminary feature selection process using the Pearson correlation coefficient was sufficient to determine the variables for use in the models. The following variables and their influence on horizontal ultraviolet irradiance were analyzed: horizontal global irradiance (IGH), clearness index (kt), solar altitude angle (α), horizontal beam irradiance (IBH), diffuse fraction (D), temperature (T), sky clearness (ε), cloud cover (Cc), horizontal diffuse irradiance (IDH), and sky brightness (Δ). The ANN models yielded results of greater accuracy than the regression models.

A space‐time model with temporal cyclostationarity for probabilistic forecasting and simulation of solar irradiance data

To establish a more sustainable future, Saudi Arabia is striving to reduce its dependency on fossil fuels and promote renewable energies. Solar energy is a major resource in Saudi Arabia because of the country's geographical location with year‐round clear skies and plentiful sunlight. However, although solar energy is a clean and safe renewable energy resource, it can be quite unpredictable. Therefore, solar irradiance needs to be forecasted and simulated as accurately as possible on both regional and national scales in the Kingdom to assist in planning for reserve usage, switching sources and short‐term power purchases. Based on an hourly solar diffuse horizontal irradiance (DHI) dataset from 45 different Saudi Arabian solar monitoring stations, this study proposes a novel spatio‐temporal model. We identify the temporal dependency of DHI as cyclostationary and incorporate this key observation in the model. This feature helps yield accurate (i.e. significantly close to the ground truth and with narrow confidence bands) probabilistic forecasts and realistic simulations in near‐future time points and at new locations. Then, the proposed model is compared with a stationary model that fails to recognize the cyclostationarity structure in the data. The proposed model produces significantly better predictions and simulations than the stationary model. Further, we calculate the photovoltaic power outputs using the simulated samples from both the models and the original observations and then compare them. The simulated samples from the proposed model afford photovoltaic power output estimates that are significantly closer to the original observed data than those from the stationary model, and therefore, they can better assist photovoltaic power output operators in assessing solar energy production and operational pre‐planning to mitigate impacts by the uncertain nature of solar irradiance.

Effect of Feature Selection on the Prediction of Direct Normal Irradiance

Solar radiation is capable of producing heat, causing chemical reactions, or generating electricity. Thus, the amount of solar radiation at different times of the day must be determined to design and equip all solar systems. Moreover, it is necessary to have a thorough understanding of different solar radiation components, such as Direct Normal Irradiance (DNI), Diffuse Horizontal Irradiance (DHI), and Global Horizontal Irradiance (GHI). Unfortunately, measurements of solar radiation are not easily accessible for the majority of regions on the globe. This paper aims to develop a set of deep learning models through feature importance algorithms to predict the DNI data. The proposed models are based on historical data of meteorological parameters and solar radiation properties in a specific location of the region of Errachidia, Morocco, from January 1, 2017, to December 31, 2019, with an interval of 60 minutes. The findings demonstrated that feature selection approaches play a crucial role in forecasting of solar radiation accurately when compared with the available data.

Improved Calculation of the Power Gain of Vertical PV Modules due to Ground Reflection Using the Ground View Factor

The calculation of the irradiance of vertically mounted building-integrated PV modules is less accurate than for PV modules that are mounted with tilt angles of less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$50^\circ$</tex-math></inline-formula> . The irradiance reflected from the ground is more relevant for vertically mounted PV modules, since the influence of ground reflection also increases with increasing module tilt angle. We compare five models that calculate the diffuse horizontal irradiance from the given global horizontal irradiance and then transpose the diffuse horizontal irradiance to the irradiance on the vertical plane facing south. We present a ground view factor with which we calculate the insolation reflected from the ground onto a PV module and compare these results with the established method for calculating ground reflection. Using our approach and measured global and diffuse horizontal irradiation, we calculate the global vertical irradiance for a 12-month period. We compare the results with the measured global vertical irradiance. By using the ground view factor, we reduce the deviation between the measurement and the calculation of the cumulative insolation at the façade by up to 9.4% compared with the irradiance calculated using established methods for ground reflectance after this 12-month period.

Comparative Analysis of Photosynthetically Active Radiation Models Based on Radiometric Attributes in Mainland Spain

The aims of this work are to present an analysis of quality solar radiation data and develop several hourly models of photosynthetically active radiation (PAR) using combinations of radiometric variables such as global horizontal irradiance (GHI), diffuse horizontal irradiance (DHI), and direct normal irradiance (DNI) from their dimensionless indices atmospheric clearness index (kt), horizontal diffuse fraction (kd), and normal direct fraction (kb) together with solar elevation angle (α). GHI, DHI, and DNI data with 1-minute frequencies in the period from 2016 to 2021 from CEDER-CIEMAT, in a northern plateau, and PSA-CIEMAT in the southeast of the Iberian Peninsula, were used to compare two locations with very different climates according to the Köppen—Geiger classification. A total of 15 multilinear models were fitted and validated (with independent training and validation data) using first the whole dataset and then by kt intervals. In most cases, models including the clearness index showed better performance, and among them, models that also use the solar elevation angle as a variable obtained remarkable results. Additionally, according to the statistical validation, these models presented good results when they were compared with models in the bibliography. Finally, the model validation statistics indicate a better performance of the interval models than the complete models.

Evaluation of models for prediction of diffuse solar radiation and comparison with satellite values

The diffuse radiation data are one of the important parameters required in solar energy projects. However, this parameter has not been measured in everywhere of Turkey. In the present study, models in the open literature for the determination of horizontal diffuse radiation with clearness index are investigated and, new models are developed for thirteen provinces over the Eastern Anatolia Region (EAR) of Turkey where the diffuse radiation values are not existing. In the monthly average, these models have a relation between the diffuse coefficient (Hd/Ho) and the diffuse fraction (Hd/H) with the daily clearness index. The most appropriate models based on NASA – Surface meteorology and Solar Energy (NASA-SSE) values are determined. For this, the diffuse radiation data taken from the NASA-SSE and calculated from the models are compared for the locations mentioned in the study. The validation of the correlations obtained to predict the diffuse radiation over the EAR for which experimental data are not available is firstly tested by using the NASA-SSE satellite data. The performances of the models are indicated with the statistical test methods as mean absolute bias error (MABE), root mean square error (RMSE), the mean bias error (MBE), and mean relative error (MRE). The results indicate that some models are reasonably well to forecast the diffuse radiation. In addition, the diffuse radiation predictions of the new correlations obtained in the study are compared with satellite data. When the diffuse radiation values taken from the NASA-SSE and calculated from the models are compared, it is seen that the Model 4 for Ağrı, Bingöl, Bitlis, Elazığ, Erzincan, Hakkari, Iğdır, Kars, and Muş, Model 14 for Erzurum, Model 20 for Malatya and Tunceli, and Model 11 for Van indicate the best performance. According to the RMSE values of twenty models in the literature and the NASA-SSE, the best result among all provinces is obtained from the Model 11 with 0.2385 MJ m−2 day−1 for Van.