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The constant scheduled load shedding in South Africa has commonly been executed in an attempt to maintain the long aging coal power plants in the country. With the rise in the reduction of fossil fuels, efforts to eradicate environmental hazards of carbon through solar photovoltaic (PV) resources to their complete prospect are in progress. South Africa, and in particular the town Alice, acquires sunshine annually, making it appropriate to harvest solar energy. This work aims to characterize solar radiation, clearness index (Kt), and diffuse fraction (Kd) in Alice, South Africa. Hourly global and diffuse solar irradiance were estimated into monthly, seasonal, and yearly variations of Kt and Kd for the years 2017–2020. The range of values for describing the daily classification of sky condition was centered on earlier studies. The cumulative frequency and frequency distribution of daily Kt was analyzed statistically in an individual month. The analyses show that the average percentage frequency of Kt within the period is 11.72% of the cloudy days, 57% of partially cloudy days, and 31.28% of clear sky days. The findings of this research show that Alice remains a key contender for solar energy conversion location, owing to its reasonably high frequency (Kt > 0.40) of clear and partially cloudy skies. Hence, it is essential to establish energy-efficiency for energy consumption and also for daily performances.

We analyze the output of various state-of-the-art irradiance models for photovoltaic systems. The models include two sun position algorithms, three types of input data time series, nine diffuse fraction models and five transposition models (for tilted surfaces), resulting in 270 different model chains for the photovoltaic (PV) system simulation. These model chains are applied to 30 locations worldwide and three different module tracking types, totaling in 24,300 simulations. We show that the simulated PV yearly energy output varies between −5% and +8% for fixed mounted PV modules and between −26% and +14% for modules with two-axis tracking. Model quality varies strongly between locations; sun position algorithms have negligible influence on the simulation results; diffuse fraction models add a lot of variability; and transposition models feature the strongest influence on the simulation results. To highlight the importance of irradiance with high temporal resolution, we present an analysis of the influence of input temporal resolution and simulation models on the inverter clipping losses at varying PV system sizing factors for Lindenberg, Germany. Irradiance in one-minute resolution is essential for accurately calculating inverter clipping losses.

We present a new model for the calculation of the diffuse fraction of the global solar irradiance for solar system simulations. The importance of an accurate estimation of the horizontal diffuse irradiance is highlighted by findings that an inaccurately calculated diffuse irradiance can lead to significant over- or underestimations in the annual energy yield of a photovoltaic (PV) system by as much as 8%. Our model utilizes a time series of global irradiance in one-minute resolution and geographical information as input. The model is validated by measurement data of 28 geographically and climatologically diverse locations worldwide with one year of one-minute data each, taken from the Baseline Surface Radiation Network (BSRN). We show that on average the mean absolute deviation of the modelled and the measured diffuse irradiance is reduced from about 12% to about 6% compared to three reference models. The maximum deviation is less than 20%. In more than 80% of the test cases, the deviation is smaller 10%. The root mean squared error (RMSE) of the calculated diffuse fractions is reduced by about 18%.

Diffuse radiation ( I f ) is one of important variables determining photosynthetic rate and carbon uptake of forest ecosystems. However, the responses of gross primary productivity (GPP) and light use efficiency (LUE) to diffuse fraction (DF) are still poorly understood. We used a 6-year dataset of carbon flux at a warm-temperate mixed plantation site in North China to explore the impacts of DF on GPP and LUE. During 2011-2017, ecosystem apparent quantum yield (α) and photosynthesis at photosynthetically active radiation (PAR) of 1800 µmol m -2 s -1 ( P 1800 ) on cloudy days were 63% and 17% higher than on clear days, respectively. Under lower vapor pressure deficit (VPD) and air temperature ( T a ) conditions, canopy photosynthesis was significantly higher on cloudy skies than on clear skies. On half-hourly scale, increased DF enhanced α and P 1800 . Daily GPP peaked at a median DF (=0.5), while daily LUE significantly increased with DF ( p <0.01). Both GPP and LUE were mainly controlled directly by DF and PAR. DF had an indirect effect on LUE and GPP mainly through PAR. At high DF levels (>0.5), the increase in LUE did not make GPP enhancement. The direct effect of DF on GPP and LUE under lower T a and VPD was more sensitive than under higher T a and VPD. When DF was incorporated into the Michaelis-Menten model, it performed well in the GPP estimation, and the determination coefficient increased by 32.61% and the root mean square error decreased by 25.74%. These findings highlight the importance of incorporating DF into carbon sequestration estimation in North China.

The accurate prediction of the solar diffuse fraction (DF), sometimes called the diffuse ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse irradiance research is discussed and then three robust, machine learning (ML) models are examined using a large dataset (almost eight years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid adaptive network-based fuzzy inference system (ANFIS), a single multi-layer perceptron (MLP) and a hybrid multi-layer perceptron grey wolf optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various solar and DF irradiance data, from Spain. The results were then evaluated using frequently used evaluation criteria, the mean absolute error (MAE), mean error (ME) and the root mean square error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance in both the training and the testing procedures.

For the various climatic zones of India, machine learning (ML) models are created in the current work to forecast monthly-average diffuse solar radiation (DSR). The long-term solar radiation data are taken from Indian Meteorological Department (IMD), Pune, provided for 21 cities that span all of India’s climatic zones. The diffusion coefficient and diffuse fraction are the two groups of ML models with dual input parameters (sunshine ratio and clearness index) that are built and compared (each category has seven models). To create ML models, two well-known ML techniques, random forest (RF) and k-nearest neighbours (KNN), are used. The proposed ML models are compared with well-known models that are found in the literature. The ML models are ranked according to their overall and within predictive power using the Global Performance Indicator (GPI). It is discovered that KNN models generally outperform RF models. The results reveal that in diffusion coefficient models perform well than diffuse fraction models. Moreover, functional form 2 is the best followed by form 6. The ML models created here can be effectively used to accurately forecast DSR in various climates.

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.

Aerosols modify the solar irradiance levels in the ultraviolet (UV) spectral band and, as a result, influence the magnitude of the biological effects of solar radiation. Also, aerosols contribute to the changes in nature (direct/diffuse) of the UV solar radiation received by a biological organism. This work quantitatively evaluates the aerosol effects on all components of solar UV irradiance (direct-normal, diffuse and global) under clear sky conditions. A note of originality is given by the wide perspective and the depth of detail in which the influence of aerosols in the UV band is captured. In addition to the common parameters, such as aerosol nature and the atmospheric aerosol loading, fine properties of aerosols, such as absorptivity and asymmetry factor, are considered. A distinctive feature of this study is given by the analysis of the effects of aerosols on the UV diffuse fraction. In general, changes in aerosol properties substantially change the direct-normal and diffuse components of UV solar irradiance and, to a lesser extent, the global component. The diffuse fraction in the UV band decreases notably with the increase of the Ångström turbidity coefficient and the Ångström exponent. Differently, the diffuse fraction is much less sensitive to the variation of the single scattering albedo and the asymmetry factor.

We investigated how radiation conditions within a tundra canopy were linked to canopy photosynthesis, and how this linkage explained photosynthetic sensitivity to sky conditions, that is total radiation and its diffuse fraction. We measured within canopy radiation at leaf scales and net CO₂ exchanges at canopy scales, under varied total irradiance and diffuse fraction, in Alaskan shrub tundra. Normalised mean radiation profiles within canopies showed no significant differences with varied diffuse fractions. However, radiation density distribution was non‐normal, being more unimodal under diffuse conditions and distinctly bimodal under direct sunlight. There was a nearly three‐fold increase in the proportion of the canopy in deep shade under direct illumination, compared to diffuse conditions. Under diffuse conditions the canopy had higher light‐use efficiency (LUE), resulting in up to 17% greater photosynthesis. The enhancement in LUE under diffuse illumination was not related to differences in the mean light profiles, but instead was due to significant shifts in the density distribution of light at leaf scales, in particular a reduced fraction of the canopy in deep shade under diffuse illumination. These results provide unique information for testing radiative transfer schemes in canopy models, and for better understanding canopy structure and trait variation within plant canopies.

Solar radiation data is essential for the development of many solar energy applications ranging from thermal collectors to building simulation tools, but its availability is limited, especially the diffuse radiation component. There are several studies aimed at predicting this value, but very few studies cover the generalizability of such models on varying climates. Our study investigates how well these models generalize and also show how to enhance their generalizability on different climates. Since machine learning approaches are known to generalize well, we apply them to truly understand how well they perform on different climates than they are originally trained. Therefore, we trained them on datasets from the U.S. and tested on several European climates. The machine learning model that is developed for U.S. climates not only showed low mean absolute error (MAE) of 23 W/m2, but also generalized very well on European climates with MAE in the range of 20 to 27 W/m2. Further investigation into the factors influencing the generalizability revealed that careful selection of the training data can improve the results significantly.