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In this study, data of the monthly average of daily global solar radiation falling on a horizontal surface, relative humidity, maximum temperature, and duration of sunshine for the city of Baghdad were collected through two sources. First, from the Iraqi Meteorological Authority (IMA) for a period extending from 1961 to 2016. The second is from NASA, for the period from 1984 to 2004. Then, four linear regression models, two single and two polynomials were formulated to calculate the values of the monthly average of daily global horizontal solar radiation (GHSR) incidents. The models calculated the monthly average of daily extraterrestrial radiation and day length, using some data provided by NASA and the IMA. To ensure the validity of the used models, a statistical test was performed for the performance of the proposed models, using the indicators mean bias Error (MBE), root mean square error (RMSE) as well as mean percentage error (MPE). The validation shows the relationship between the measured and computed values (through the analysis of the results), where a great convergence was found between the measured and calculated values. This means that the proposed models can be adapted to predict global solar radiation. The highest values of measured solar radiation were during the month of June, which were 28.555 and 27.280 MJ/m2/day from the IMA and NASA, respectively. The same applies to the radiation calculated using the four empirical models. The month of June was the highest in terms of solar radiation values. The radiation values were 28.947, 26.315, 29.699, and 26.716 MJ/m2/day for the first, second, third, and fourth models, respectively. The lowest values of measured and calculated radiation were during the month of December. Always, radiation measured by the IMA was greater than those of NASA, as well as the values of radiation calculated in the two IMA-based models were greater than the other two NSA-based models. In the absence of a method for measuring the diffuse and direct (beam) solar radiations, as well as the lack of such values by meteorological authorities, and its paramount importance, they were reported to mathematically calculate them in this study. The values of statistical indicators RMSE; MJ/m2/day, MBE; MJ/m2/day and MPE% were (0.4769, 0.0164, 0.2207), (0.8641, 0.1773, -0.9680), (0.6420, 0.3996, -1.1487), (0.9604, 0.218, -1.0225) for the first, second, third and fourth models, respectively. According to the results of the statistical test, it can be indicated that the single linear regression model, based on the IMA’s data (model No.1), is the most accurate to calculate global solar radiation for Baghdad City.

The world’s energy consumption is being replaced by renewable energies in large part because of the depletion of fossil fuels and the acceleration of environmental change. This study reports the amount of inward solar radiation in the date range from January 2018 to December 2022 in the Gregorian Calendar for certain areas in Bahir Dar, Ethiopia: 37°E and 11.6°N. On the horizontal surface of the case area, the month with the highest global radiation (monthly average daily) is March, at approximately 42.56 MJ/m 2 . day; June has the lowest diffuse radiation, at 16.2 MJ/m 2 .day. Furthermore, April had the most global radiation (monthly average hourly) on the horizontal surface, measuring 9.09 MJ/m2.hour, while June had the lowest diffuse radiation, measuring 2.3 MJ/m2.hour. In addition, this study predicts the beam, diffuse, and total radiation on the tilted collector using the total available horizontal radiation on a monthly and hourly basis. According to the research, the output of the radiation on the tilted surface towards the equator in the northern hemisphere, azimuth angle, γ = 0°, shows that the highest possible total radiation (monthly average daily) is 48.3 MJ/m 2 . day (January) and the highest possible total radiation (monthly average hourly) in February, 9.14 MJ/m 2 .hour at 1:00 p.m.

This study examined the surface global solar irradiation variability in the southern Algerian region, with regard to the regional climatic and environmental features. The statistical analysis used ground data acquired in measurement stations operated by a technical platoon of meteorologists and physicists at the Renewable Energy Research Unit in the Saharan Environment (URER/MS). A graphical statistical analysis of long-term solar irradiance data is performed using appropriate visualizations, such as time series plots, box plots, and histograms. The data used is that of the decade (2011–2020), and the analysis is extended to the last 30 years (1990–2020) based on solar radiation data handed by the National Aeronautics and Space Administration (NASA). The present study takes advantage of a unique and high-quality dataset consisting of 10 years of concurrent records of global solar irradiation in the South Algerian region. The results of this study pointed out a remarkable variability in seasonal and annual scales and confirmed that this region has enormous solar energy potentiality, where the average periodic diurnal energy for global solar radiation measured on a horizontal plane exceeds 6.16 kWh/m 2 /day and an additive total energy of 2. 2 MWh/m 2 /time on average.

Solar energy predictive models designed to emulate the long-term (e.g., monthly) global solar radiation (GSR) trained with satellite-derived predictors can be employed as decision tenets in the exploration, installation and management of solar energy production systems in remote and inaccessible solar-powered sites. In spite of a plethora of models designed for GSR prediction, deep learning, representing a state-of-the-art intelligent tool, remains an attractive approach for renewable energy exploration, monitoring and forecasting. In this paper, algorithms based on deep belief networks and deep neural networks are designed to predict long-term GSR. Deep learning algorithms trained with publicly-accessible Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data are tested in Australia’s solar cities to predict the monthly GSR: single hidden layer and ensemble models. The monthly-scale MODIS-derived predictors (2003–2018) are adopted, with 15 diverse feature selection approaches including a Gaussian Emulation Machine for sensitivity analysis used to select optimal MODIS-predictor variables to simulate GSR against ground-truth values. Several statistical score metrics are adopted to comprehensively verify surface GSR simulations to ascertain the practicality of deep belief and deep neural networks. In the testing phase, deep learning models generate significantly lower absolute percentage bias (≤3%) and high Kling–Gupta efficiency (≥97.5%) values compared to the single hidden layer and ensemble model. This study ascertains that the optimal MODIS input variables employed in GSR prediction for solar energy applications can be relatively different for diverse sites, advocating a need for feature selection prior to the modelling of GSR. The proposed deep learning approach can be adopted to identify solar energy potential proactively in locations where it is impossible to install an environmental monitoring data acquisition instrument. Hence, MODIS and other related satellite-derived predictors can be incorporated for solar energy prediction as a strategy for long-term renewable energy exploration.

The intermittency of solar power presents significant challenges for grid and energy systems, primarily owing to the unstable and chaotic characteristics of solar radiation time series caused by the combination of seasonality and stochasticity. Currently, there is a lack of simple and easily interpretable methods for describing or transforming solar radiation time series that can directly benefit various solar energy applications. In this study, a deseasonalization method for solar radiation time series based on a transformation matrix is introduced. This method is based on the assumption that daily solar radiation patterns can be scaled by day length and radiation intensity and involves transforming daily solar radiation patterns to eliminate differences due to seasonal effects and geographical location. The proposed method is developed and validated using long-term hourly data collected from five observation stations in Japan: Abashiri, Sapporo, Tokyo, Hiroshima, and Fukuoka, spanning the year from 2000 to 2022. The results indicate that the deseasonalized solar radiation time series exhibits three characteristics: (1) improved correlation in time series, (2) comparable radiation patterns, and (3) seasonal scalability. This study provides a foundational approach to simplifying the solar radiation time series, offering potential for broader applications in solar energy forecasting.

Data for solar radiation resources play a pivotal role in assessing the energy yield capability of solar applications. A nationwide database for the typical meteorological year from the 30 weather stations of the Central Weather Bureau (CWB) in Taiwan is used to determine the spatial distribution of global radiation over the terrain of Taiwan. There is no available beam radiation information in daily reports from all CWB stations. Information on the diffuse fraction for all CWB stations is estimated using three available correlation models that account for topographical and geographical effects in Taiwan. The databases for beam radiation are generated using these estimated diffuse fractions. The mappings of global and beam radiation on the Taiwanese mainland are performed with databases from 24 CWB stations using the residual kriging method. There are no mappings of the remote islands, where six CWB stations are located. The databases for global and beam radiation for these six CWB stations are applied to nearby remote islands. The effects of topography and geography on the distributions of global and beam radiation are discussed. The spatial distributions of solar radiation presented are good scientific references for assessing the performances of solar energy systems in Taiwan.

There are various empirical models used in the estimation of global solar radiation; however, knowledge of direct and diffuse solar radiation is insufficient. Global solar radiation is the sum of direct and diffuse solar radiation, and a method that calculates global solar radiation from the estimated direct and diffuse solar radiation was further proposed in this study. The observed daily solar radiation and meteorological data from 97 stations during 1993–2016 were used for the analysis, and the results indicated that the concave-shaped relationship with relative sunshine duration was more obvious for direct solar radiation than for global solar radiation, while an inverted u-shaped relationship was found for diffuse solar radiation. Generally, the performances of empirical models in estimating direct and diffuse solar radiation were worse than the estimation of global solar radiation. However, because the bias of estimated direct and diffuse solar radiation was partially offset, the results in this study indicated that global solar radiation can be better calculated from the estimated direct and diffuse solar radiation when compared with the best performed empirical model, especially in data-scarce regions. The results of this study will aid in better estimations and understanding of the variations in global solar radiation, as well as direct and diffuse solar radiation.

Accurate estimation of the spatiotemporal variations of solar radiation is crucial for assessing and utilizing solar energy, one of the fastest‐growing and most important clean and renewable resources. Based on observations from 2,379 meteorological stations along with scare solar radiation observations, the random forest (RF) model is employed to construct a high‐density network of daily global solar radiation (DGSR) and its spatiotemporal variations in China. The RF‐estimated DGSR is in good agreement with site observations across China, with an overall correlation coefficient (R) of 0.95, root‐mean‐square error of 2.34 MJ/m2, and mean bias of −0.04 MJ/m2. The geographical distributions of R values, root‐mean‐square error, and mean bias values indicate that the RF model has high predictive performance in estimating DGSR under different climatic and geographic conditions across China. The RF model further reveals that daily sunshine duration, daily maximum land surface temperature, and day of year play dominant roles in determining DGSR across China. In addition, compared with other models, the RF model exhibits a more accurate estimation performance for DGSR. Using the RF model framework at the national scale allows the establishment of a high‐resolution DGSR network, which can not only be used to effectively evaluate the long‐term change in solar radiation but also serve as a potential resource to rationally and continually utilize solar energy. Key Points The RF model exhibits high accuracy and considerable potential in projecting DGSR Importance of possible variables for estimating DGSR is identified by the RF model Spatiotemporal variations of DGSR across China are constructed from high‐density meteorological observations

Solar radiation has important impacts on buildings such as for cooling/heating load forecasting, energy consumption forecasting, and multi-energy complementary optimization. Two types of solar radiation data are commonly used in buildings: radiation data in typical meteorological years and measured radiation data from meteorological stations, both of which are types of historical data. However, it is difficult to predict the hourly global solar radiation, which affects the application of relevant prediction models in practical engineering. Most existing methods for predicting hourly global solar radiation have issues such as difficulty in obtaining input parameters or complex data processing, which limits their practical engineering applications. This study proposed a simplified method to accurately predict the hourly horizontal solar radiation using extraterrestrial solar radiation, weather types, cloud cover, air temperature, relative humidity, and time as the input parameters. The back-propagation network, support vector machine, and light gradient boosting machine (LightGBM) models were used to establish the prediction model, and Shapley additive explanations were used to analyze the relationship between the input variables and the prediction results to simplify the structure of the prediction model. Taking Lanzhou New District in Gansu Province as an example, the results showed that the LightGBM model performed the best, with the root mean square error of 126.1 W/m2. Shapley additive explanations analysis showed that weather type was not a significant factor in the LightGBM model. Therefore, the weather type was removed from the LightGBM model and the root mean square error was 135.2 W/m2. The results showed that extra-terrestrial radiation and limited weather forecast parameters can be used to predict hourly global solar radiation with satisfactory prediction results.

Solar radiation is an important parameter that affects the atmosphere-earth thermal balance and many water and soil processes such as evapotranspiration and plant growth. The modeling of the daily and monthly solar radiation by Gaussian process regression (GPR) with K-fold cross-validation model has been discussed recently. This study evaluated different neural models such as artificial neural network (ANN), support vector machine (SVM), adaptive network-based fuzzy inference system (ANFIS), and multiple linear regression (MLR) for estimating the global solar radiation (daily and monthly) with K-fold cross-validation method. For the appropriate comparison of the models, the randomized complete block (RCB) design applied in the training and test phases. Also, different data sets were evaluated by K-fold cross-validation in each model. The results showed that radial basis function (RBF) model has the lowest error for estimating the monthly and daily solar radiation. In this study, the result of RBF was compared with the GPR models. The conclusion indicated that RBF methodology can predict solar radiation with higher accuracy relative to the GPR model. The results of yearly solar radiation estimation (2009–2014) showed that the RBF model can estimate solar radiation with the MAPE and RMSE of 5.1% and 0.29, respectively. Also, the coefficient of correlation (R2) between actual and estimated values throughout the year is 98% and can be used by the engineers and other researchers for solar and thermal applications.