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Solar energy assessment using remote sensing technologies
Abstract
About 20% of the final energy consumed in Europe is used in buildings. The active and passive use of solar energy is an approach to reduce the fossil energy consumption and the greenhouse gas emissions originated by buildings. Consideration of solar energy technologies in urban planning demands accurate information of the available solar resources. This can be achieved by the use of remote sensing data from geostationary satellites which show a very high spatial and a sufficient temporal resolution compared to ground station data. This paper gives a brief introduction to the HELIOSAT method applied to derive surface solar irradiance from satellite images and shows examples of applications: The use of daylight in buildings, the generation of correlated time series of solar irradiance and temperature as input data for simulations of solar energy systems and a short-term forecast of solar irradiance which can be used in intelligent building control techniques. Finally an outlook is given on potential improvements expected from the next generation of European meteorological satellites Meteosat Second Generation (MSG).
... Many studies have been conducted to estimate the global horizontal irradiance (GHI) from geostationary satellites images (Sengupta et al., 2018;Qu et al., 2017;Hammer et al., 2003;Garniwa et al., 2021). Both physical and semi-empirical models are extensively used in estimating GHI while simple empirical methods are barely applied due to their inferior performance caused by the lack of generality (Garniwa et al., 2021;Laguarda et al., 2020). ...
... On the other hand, semi-empirical methods typically deal with the irradiance attenuation of atmospheric constituents and cloud extinction separately, with a clear-sky model for clear-sky irradiance and a cloud index (CI) derived from satellite image to account for cloud attenuation (Kleissl, 2013;Hammer et al., 2003). Heliosat method series (Beyer et al., 1996;Rigollier et al., 2004;Mueller et al., 2004) are examples of semi-empirical models, which offer easy implementation, fast calculation and operation (Garniwa et al., 2021). ...
... Many clear-sky models have been used in semi-empirical models for GHI estimation, such as the Ineichen-Perez model in the operational model (SUNY model) developed by Perez et al. (2002), the McClear model in the work of Jia et al. (2021), and the REST2 (Gueymard, 2008) model in Solcast (Solcast, 2021). There are also different methods proposed to calculate the GHI based on clear-sky index (CSI) and CI in the literature (Hammer et al., 2003;Perez et al., 2002;Mueller et al., 2012). ...
Semi-empirical satellite method is widely used in estimating global horizontal irradiance (GHI), where various clear-sky models, cloud index (CI) and clear-sky index (CSI) derivation methods are available. This study aims to optimize the semi-empirical satellite model for 5-minute GHI estimation from four aspects: satellite-bands, CI and CSI derivation methods, and clear-sky models. The results show that it achieves better GHI estimates using the blue band, CI derived from monthly fixed upper and lower bounds, and a piecewise CI-to-CSI function. There is no significant difference in all-sky GHI estimation for the clear-sky models regarding normalized root mean squared error (nRMSE, 25.19%–25.53%), which is comparable with the referenced physical model. Clouds cause the largest uncertainty, where the nRMSE is in the range of 37.60%–38.36% in cloudy days and 31.12%–31.54% in cloudy periods. In the application of semi-empirical method with different clear-sky models, Ineichen–Perez has the highest bias of -4.62% in clear days and -3.93% in cloudless periods. REST2 outperforms McClear with slightly lower nRMSE and normalized mean bias error (nMBE) under all sky conditions. McClear is recommended due to its global availability. Modified Ineichen–Perez produces the lowest nRMSE and nMBE using clear-sky GHI as the GHI estimates for clear periods, therefore has the potential for improvements in physical methods.
... Over recent decades the overall need for an accurate spatiotemporal nowcasting of weather has increased due to the rising importance of renewable energies and the fluctuating energy supply due to the short-term variation in the governing atmospheric elements (e.g., clouds and solar radiation) [1][2][3]. Particularly if renewable energies are integrated into the grid it is very important to correctly forecast the weather, as well as power needs, to prevent grid instabilities. Instabilities may occur as solar energy and wind energy have a major impact on the load flows and for this reason, forecasts have to become more precise, especially in the short-term range of 0-4 h [4][5][6][7]. ...
... H 2 O is taken from the European Centre for Medium-Range Weather Forecasts (ECMWF) [36]. 3. ...
Due to the integration of fluctuating weather-dependent energy sources into the grid, the importance of weather and power forecasts grows constantly. This paper describes the implementation of a short-term forecast of solar surface irradiance named SESORA (seamless solar radiation). It is based on the the optical flow of effective cloud albedo and available for Germany and parts of Europe. After the clouds are shifted by applying cloud motion vectors, solar radiation is calculated with SPECMAGIC NOW (Spectrally Resolved Mesoscale Atmospheric Global Irradiance Code), which computes the global irradiation spectrally resolved from satellite imagery. Due to the high spatial and temporal resolution of satellite measurements, solar radiation can be forecasted from 15 min up to 4 h or more with a spatial resolution of 0.05 ∘ . An extensive validation of this short-term forecast is presented in this study containing two different validations based on either area or stations. The results are very promising as the mean RMSE (Root Mean Square Error) of this study equals 59 W/m 2 (absolute bias = 42 W/m 2 ) after 15 min, reaches its maximum of 142 W/m 2 (absolute bias = 97 W/m 2 ) after 165 min, and slowly decreases after that due to the setting of the sun. After a brief description of the method itself and the method of the validation the results will be presented and discussed.
... Such regression methods are simple and easily implemented [5,6], but the relationship determined for a specific region is usually difficult to apply in other regions [7]. This may apply particularly to relationships established by applying AI algorithms, while physics -based relationships, such as [5], are more generally applicable [8], [9], [10], [11], [12]. Parameterization methods are based on physical processes, but the estimate of the SSI as a function of water vapor, aerosol optical depth (AOD), and thickness of the ozone layer is simplified [13], [14], [15]. ...
... These methods based on LUTs avoid using complex input parameters. There are also other estimation methods, such as the method of combining empirical relationships with physical methods [8,9], optimization methods [20] and machine learning methods [21][22][23]. ...
Accurate knowledge of the at-surface solar irradiance (SSI) is essential to retrieving surface and atmospheric properties using satellite measurements of back-scattered and reflected radiance. The latter is affected by surface-atmosphere interactions, including the effects of terrain. The SSI is affected by the same processes. This study proposes a method to estimate the components of instantaneous surface solar irradiance: direct, isotropic and circumsolar diffuse, and terrain irradiance, which is expected to improve the simultaneous retrieval of Aerosol Optical Depth (AOD) and surface reflectance. The method takes into account the coupled effects of topography and atmosphere by combining parametrization and the Look-Up Table (LUT) approaches. The method was applied to rugged terrain over the Tibetan Plateau using MODIS atmosphere and surface data, ERA5 reanalysis data, CALIOP aerosol data and a Digital Elevation Model (DEM). The results showed that the SSI estimates were in satisfactory agreement with ground observations at four stations over the Tibetan Plateau in 2018 with R
2
values of 0.61, 0.44, 0.41, and 0.49, respectively, and RMSE of 205.7 W/m
2
, 176.9 W/m
2
, 186.0 W/m
2
, and 201.2 W/m
2
, respectively. Estimations of the diffuse irradiance were evaluated separately against the only available in-situ observations at the Dali Station and the results were better than our SSI estimates with R
2
, RMSE, and relative BIAS being 0.71, 94.98 W/m
2
, and 31%, respectively. The isotropic and circumsolar diffuse irradiance accounted for 37.57% and 7.68% of the total annual SSI respectively, while diffuse irradiance accounted for 46.48% of the total annual SSI. Under clear skies, every 0.1 increase in AOD caused about a 35 W/m
2
increase in diffuse irradiance and a decrease of about 25 W/m
2
of SSI.
... In terms of solar resource assessment, this is critically vital, because even the size of large utilityscale PV plants is sub-kilometer. The performance is determined by two key parameters, i.e., cloud index (CI) and clear-sky index (K c ), which separately represent cloud effect and potential attenuation by aerosol, water vapor, and other atmospheric constituents [13,17,26]. Therefore, the most critical aspect of the semi-empirical methods is to establish an accurate and robust parameterization of CI and K c from satellite measurements and other auxiliary data. ...
... The upper bound of the dynamic range, ρ t,c (i, j), is determined for each pixel and time window, or alternatively, using a single value for the target region that is dependent on the radiometers [13,47]. A histogram of all apparent reflectance throughout the time window in China is generated. ...
... The clear-sky index (K c ) (Cano et al., 1986) value was determined by using the original Heliosat method, based on its relationship with the cloud index (Hammer et al., 2003), as follows: ...
... To test such distribution, we split datasets into clear-sky, intermediate cloudy-sky, and cloudy-sky conditions. These three different sky conditions were derived from the clear-sky index (K c ) value that was calculated from observation by the ratio of GHI/E c (Hammer et al., 2003). Index values greater than 0.8 were classified as a clear-sky condition, those between 0.4 and 0.8 as an intermediate cloudy-sky condition, and those less than 0.4 as a cloudy-sky condition (Babar et al., 2019;Engerer and Mills, 2014;Smith et al., 2017). ...
The increased interest in the sources of renewable electricity has drawn attention to the rapidly developing solar energy sector owing to its high cost-benefit ratio. To accurately calculate the potential electricity output of photovoltaic (PV) panels, the global horizontal irradiance (GHI) and diffuse horizontal irradiance (DHI) must be known. The worldwide presence of satellite imagery provides an efficient way to estimate current and historical GHI instead of using the high time- and cost-consuming in-situ measurement. The most of past studies utilized more satellite bands as the inputs for machine learning algorithms to estimate GHI. The further estimation of DHI was usually based on weather-related parameters. Thus, there is a research gap in using only one satellite band for both GHI and DHI estimations. Therefore, this study used machine learning algorithms to estimate GHI and DHI, with inputs delivered from the Heliosat model based on band 3 of Himawari-8 satellite imageries. The results were compared with the original and site-adapted Heliosat models and seven DHI separation models. The results indicated that the machine learning models were capable of performing with the same accuracy as the Heliosat models. However, their performance was better while estimating DHI, in which case they outperformed even the best separation model. Higher accuracy and precision were observed in those models where the additional solar zenith at time t+1h was used together with other input features. This highlighted the possibility of using only one satellite band together with the calculated solar position variables as the input. Overall, this research has established a new method for estimating GHI and DHI with high confidence based on satellite imageries and the Heliosat model through the application of machine learning techniques.
... The experiment was conducted in aerobic conditions (37 °C) and four types of pathogenic bacteria were grown. The agar well diffusion method was used according to the method described by Hammer et al., (2003) through utilization of Muller Hinton agar medium to evaluate the extracts' antibacterial potency. ...
The potential use of lemongrass plant essential oil extract as an antibacterial and anti-mildew agent, which is known for its inhibitory action against microorganisms that cause food spoilage and fungal food components, was investigated in the current research, especially the parasitic Aspergillus flavus (A.) and Aspergillus ochraceus (A). It was used three different types of perfume for the lemongrass: (0.1, 0.2, 0.5) % As well as the comparative treatment, All treatments were stored in the refrigerator at (5 degrees C) for 28 days, and the sensory and chemical properties continued during the storage period, as the results of yogurt to all http://dx. treatments in prolonging the storage life of yogurt are superior to control treatment it was excelled as adding to the essential oil by 0.2 and 0.5% was significantly superior in terms of color, taste, flavor and general appearance. Both treatments were superior to the other treatments and demonstrated a significant difference from the control treatment. Improving the sensory characteristics and extending the shelf life of yogurt may be due to the significant effect of the extract in reducing the total number of bacterial colonies, yeasts and molds by a large percentage (P<0.05) in all addition ratios of essential oil. The essential oil extract of lemongrass had a clear inhibitory effect on a number of types of pathogenic bacteria, such as E. coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Bacillus subtillis, but this effect differed depending on the type of bacteria and the added percentage, as it was noted that treatment with the essential oil extract at a rate of 0.1% gave The best results are in determining the MIC and the lowest effective concentrations in obtaining effective bacterial inhibition.
... SARAH (Pfeifroth et al., 2023a) uses a semi-empirical algorithm based on the Heliosat method (Hammer et al., 2003). The effective cloud albedo is derived from the observations of MVIRI (1983MVIRI ( -2005 and SEVIRI (2006-present) instruments onboard Meteosat First Generation (MFG) and Meteosat Second Generation (MSG) prime satellites, respectively. ...
Satellite products provide the best way to monitor the solar radiation reaching the Earth's surface on a global scale. However, their capability to monitor solar radiation trends needs to be constantly evaluated. This depends on their temporal stability and the accurate representation of all processes driving solar radiation. This study evaluates these aspects by comparing and cross-comparing different solar radiation products (ERA5, CAMS-RAD 4.6, SARAH-3, CLARA-A3, CERES-EBAF 4.2) against in-situ measurements over Europe. All products show a moderate positive bias over Europe but strong differences in their root mean squared deviation (RMSD) related to their different cloud transmittance models. Geostationary-based products (SARAH-3, CAMS-RAD 4.6) provide the smallest RMSD closely followed by CLARA-A3, whereas ERA5 shows a large RMSD due to random errors in cloud transmittance. All products show an increase in surface solar radiation, or brightening, over the last 40 years over Europe, but the magnitude of the trends and their spatiotemporal variability differ between products. Despite finding temporal inhomogeneities in some products, the different trends are mostly due to different aerosol modeling approaches implemented by each product. Both SARAH-3 (+2.3 W∕m 2 ∕decade, 2001-22) and CERES-EBAF 4.2 (+2.2 W∕m 2 ∕decade, 2001-22) provide the most consistent trends compared to in-situ data, showing that after stabilizing in the late 2000s, brightening is particularly recovering in Western Europe. In-situ measurements show a reduction of aerosol optical depth from 2001 to 2022 that has been accentuated in the last 10 years, particularly in Western Europe. This would be consistent with the hypothesis that brightening recovery is driven by an aerosol reduction, though other analyses suggest that clouds also play a role in this recovery. More work is needed to understand the contribution of aerosols to solar radiation trends and the exact aerosol effects represented by each solar radiation product.
... Thus, it allows for a deduction of the irradiance passing through a cloud at a certain point, that is, pixel. [36] The modeled cloud index values were then scaled down to an eight-bit representation linearly mapped to integer numbers from 0 to 255. Figure 4 shows the corresponding image. The transparency channel carries the information on shadowing intensity. ...
The rapidly increasing share of fluctuating electricity from photovoltaics calls for accurate approaches to estimate cloud motion, the primary source for the varying power supply. While local sensor networks are prominent in targeting forecast horizons too short for image-based methods, they have minimal spatial coverage. This work presents the first step towards expanding those approaches to spatially scalable sensor networks: With the motivation of using automotive light sensors as a sensor network, two excerpts from a microscopic traffic simulation serve as simulative sensor networks. A fractal-based cloud shadow pattern passes the sensor network areas with defined velocities and directions, which shall be estimated using the cumulative mean absolute error method. The evaluation results indicate that the more extensive observation areas compensate for the dynamics in the sensor network when compared to a reference work with a static sensor grid. Furthermore, this work shows how the estimates deteriorate with lower vehicle penetration rates (PR) and longer building shadows due to a lower solar elevation angle. At a penetration rate of 40%, the root mean square errors for both sensor networks are still below 5 ms−1. In conclusion, the spatiotemporal characteristics of a vehicle network offer a potential for estimating cloud movements.
... A clear-sky index close to 1 would imply a sky without clouds, and a value close to 0 would imply a sky covered by optically thick clouds. Over a short time period under broken-cloud conditions, the clear-sky index might be greater than 1, meaning an enhancement effect [40][41][42][43][44]. The use of the clear-sky index is interesting in remote sensing because it is a quantity, or equivalently, a cloud index, that is directly derived from satellite images by many methods [17,20,22,[45][46][47]. ...
Several studies proposed relationships linking irradiances in the photosynthetically active radiation (PAR) range and broadband irradiances. A previous study published in 2024 by the same authors proposes a linear model relating clear-sky indices in the PAR and broadband ranges that has been validated in clear and overcast conditions only. The present work extends this study for broken-cloud conditions by using ground-based measurements obtained from the Surface Radiation Budget Network in the U.S.A. mainland. As expected, the clear-sky indices are highly correlated and are linked by affine functions whose parameters depend on the fractional sky cover (FSC), the year, and the site. The previous linear model is also efficient in broken-cloud conditions, with the same level of accuracy as in overcast conditions. When this model is combined with a PAR clear-sky model, the result tends to overestimate the PAR as the FSC decreases, i.e., when fewer and fewer scattered clouds are present. The bias is equal to 1 W m⁻² in overcast conditions, up to 18 W m⁻² when the FSC is small, and 6 W m⁻² when all cloudy conditions are merged. The RMSEs are, respectively, 5, 24, and 15 W m⁻². The linear and the clear-sky models can be combined with estimates of the broadband irradiance from satellites to yield estimates of PAR.
... Generally solar energy is measured in terms of solar radiation data with the help of measuring instruments mounted on specific locations. Remote Sensing and GIS techniques have enabled researchers to estimate solar radiation data at locations where measuring equipment are not available (Hammer et al., 2003, Hofierka and Suri, 2002, Perez et al., 2002, Polo and Perez, 2019. ...
The present study utilizes time series solar radiation data for 34 years (i.e., January 1984 to December 2017) from NASA's POWER project to perform a spatial assessment and simulation of solar radiation in and around New Delhi, India. Solar radiation forecasting was accomplished on a 0.5º x 0.5º spatial extent utilizing two approaches: the ARIMA models and the GIS-based solar radiation spatial analyst tools in ArcGIS. The solar radiation estimates from the two approaches were validated using WRMC-BSRN (World radiation monitoring center-Baseline surface radiation network) data. Solar radiation estimates obtained from the two approaches when compared with the actual ground measured data, revealed that ARIMA model estimated solar radiation with higher accuracy in terms of R2 value as 0.9108, whereas ArcGIS solar analyst-based estimates yielded R2 value of 0.6408. The study presents the applicability of ARIMA models in the prediction of solar radiation and highlight their potential in the design and functioning of energy systems.
... It explicitly separates clouds from snow and includes a physically based calculation of the clear-sky atmosphere (for details see Stöckli 2013). It derives a combined cloud index based on the visible and infrared channels, from which the clear-sky index is subsequently determined with an empirical relationship (Hammer et al. 2003). Clear-sky index data for this paper were specifically retrieved over Austria and surroundings at the resolution of SEVIRI's visible channel (approximately 1.5 km over the domain of interest). ...
Grid datasets of sunshine duration at high spatial resolution and extending over many decades are required for quantitative applications in regional climatology and environmental change (e.g., modelling of droughts and snow/ice covers, evaluation of clouds in numerical models, mapping of solar energy potentials). We present a new gridded dataset of relative (and derived absolute) sunshine duration for Austria at a grid spacing of 1 km, extending back until 1961 at daily time resolution. Challenges in the dataset construction were consistency issues in the available station data, the scarcity of long time series, and the high variation of cloudiness in the study region. The challenges were addressed by special efforts to correct evident breaks in the station series and by adopting an analysis method, which combines station data with satellite data. The methodology merges the data sources non-contemporaneously, using statistical patterns distilled over a short period, which allowed involving satellite data even for the early part of the study period. The resulting fields contain plausible mesoscale structures, which could not be resolved by the station network alone. On average, the analyses explain 47% of the spatial variance in daily sunshine duration at the stations. Evaluation revealed a slight systematic underestimation (− 1.5%) and a mean absolute error of 9.2%. The average error is larger during winter, at high altitudes, and around the 1990s. The dataset exhibits a conditional bias, which can lead to considerable systematic errors (up to 15%) when calculating sunshine-related climate indices.
... Where the results of the compounds showed their different efficacy against negative and positive staining bacteria, while the Dy +3 complexes did not show any inhibitory activity against the laboratory bacterial species [23]. The data are shown in Table 5. ...
In this work, new complexes of some lanthanide ions metals, such as (La +3 , Nd +3 , Er +3 , Gd +3 and Dy +3) with Schiff bases that derived from the antipyrine compound were synthesized. This ligand can be synthesized from condensation dimedone with 4-aminoanitpyrine in microwave irradiation and were identified using (FT-IR spectroscopy, UV-Visible spectroscopy, CHNO analysis, LC-mass, 1 H-NMR, 13 C-NMR and TGA analysis). The complexes were characterized by FT-IR, UV-Visible, CHNO analysis, conductivity measurement, magnetic susceptibility, and theromgravimatric analysis. From stoichiometry of metal to ligand and all measurements was proposed formula of complexes and a molar ratio (2-2) (metal-ligand) to give the. The bioactivity of the prepared (L) and its complexes were assessed using antibacterial activity, and the results revealed significant activity against some fungi and bacteria.
... The experiment was conducted in aerobic conditions (37 °C) and four types of pathogenic bacteria were grown. The agar well diffusion method was used according to the method described by Hammer et al., (2003) through utilization of Muller Hinton agar medium to evaluate the extracts' antibacterial potency. ...
The potential use of lemongrass plant essential oil extract as an antibacterial and anti-mildew agent, which is known for its inhibitory action against microorganisms that cause food spoilage and fungal food components, was investigated in the current research, especially the parasitic Aspergillus flavus (A.) and Aspergillus ochraceus (A). It was used three different types of perfume for the lemongrass: (0.1, 0.2, 0.5) % As well as the comparative treatment, All treatments were stored in the refrigerator at (5 degrees C) for 28 days, and the sensory and chemical properties continued during the storage period, as the results of yogurt to all treatments in prolonging the storage life of yogurt are superior to control treatment it was excelled as adding to the essential oil by 0.2 and 0.5% was significantly superior in terms of color, taste, flavor and general appearance. Both treatments were superior to the other treatments and demonstrated a significant difference from the control treatment. Improving the sensory characteristics and extending the shelf life of yogurt may be due to the significant effect of the extract in reducing the total number of bacterial colonies, yeasts and molds by a large percentage (P<0.05) in all addition ratios of essential oil. The essential oil extract of lemongrass had a clear inhibitory effect on a number of types of pathogenic bacteria, such as E. coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Bacillus subtillis, but this effect differed depending on the type of bacteria and the added percentage, as it was noted that treatment with the essential oil extract at a rate of 0.1% gave The best results are in determining the MIC and the lowest effective concentrations in obtaining effective bacterial inhibition.
... The experiment was conducted in aerobic conditions (37 °C) and four types of pathogenic bacteria were grown. The agar well diffusion method was used according to the method described by Hammer et al., (2003) through utilization of Muller Hinton agar medium to evaluate the extracts' antibacterial potency. ...
The potential use of lemongrass plant essential oil extract as an antibacterial and anti-mildew agent, which is known for its inhibitory action against microorganisms that cause food spoilage and fungal food components, was investigated in the current research, especially the parasitic Aspergillus flavus (A.) and Aspergillus ochraceus (A). It was used three different types of perfume for the lemongrass: (0.1, 0.2, 0.5) % As well as the comparative treatment, All treatments were stored in the refrigerator at (5 degrees C) for 28 days, and the sensory and chemical properties continued during the storage period, as the results of yogurt to all http://dx. treatments in prolonging the storage life of yogurt are superior to control treatment it was excelled as adding to the essential oil by 0.2 and 0.5% was significantly superior in terms of color, taste, flavor and general appearance. Both treatments were superior to the other treatments and demonstrated a significant difference from the control treatment. Improving the sensory characteristics and extending the shelf life of yogurt may be due to the significant effect of the extract in reducing the total number of bacterial colonies, yeasts and molds by a large percentage (P<0.05) in all addition ratios of essential oil. The essential oil extract of lemongrass had a clear inhibitory effect on a number of types of pathogenic bacteria, such as E. coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Bacillus subtillis, but this effect differed depending on the type of bacteria and the added percentage, as it was noted that treatment with the essential oil extract at a rate of 0.1% gave The best results are in determining the MIC and the lowest effective concentrations in obtaining effective bacterial inhibition.
... SARAH-2 exploits the images from Meteosat geostationary satellites centered at 0°longitude, which covers the region (disk) from 65°W to 65°E, and 65°S-65°N. SARAH uses the HELIOSAT algorithm (Hammer et al., 2003) to estimate the effective cloud albedo from the visible channels of the MVIRI (Meteosat First Generation, 1983 and SEVIRI (Meteosat Second Generation, 2005-present) instruments. The clear-sky index is estimated from the effective cloud albedo with a semi-empirical relationship. ...
Satellite and in situ sensors do not observe exactly the same measurand. This introduces a mismatch between both types of measurements in the spatial or temporal. The mismatch differences can be the dominant component in their comparison, so they have to be removed for an adequate validation of satellite products. With this aim, we propose a methodology to characterize the mismatch between satellite and in situ measurements of surface solar radiation, evaluating the impact of the mismatch on validations. The Surface Solar Radiation Data Set—Heliosat (SARAH‐2) and the Baseline Surface Radiation Network are used to characterize the spatial and temporal mismatch, respectively. The mismatch differences in both domains are driven by cloud variability. At least 5 years are needed to characterize the mismatch, which is not constant throughout the year due to seasonal and diurnal cloud cover patterns. Increasing the mismatch can artificially improve the validation metrics under some circumstances, but the mismatch must be always minimized for a correct product assessment. Finally, we test two types of up‐scaling methods based on SARAH‐2 in the validation of degree‐scale products. The fully data‐driven correction removes all the mismatch effects (systematic and random) but fully propagates SARAH‐2 uncertainty to the corrections. The model‐based correction only removes the systematic mismatch difference, but it can correct measurements not covered by the high‐resolution data set and depends less SARAH‐2 uncertainty.
... Modern techniques and methodologies, like those used in the Satellite Application Facility on Climate Monitoring (CM-SAF) all-sky data, effectively simulate cloud reflectance and attenuation effects on radiative flux, despite cloud complex microphysical and optical properties. Several estimation approaches exist, including empirical relationships, physical principles, optimization techniques, and machine learning models [36][37][38][39][40][41]. ...
Accurate and continuous estimation of surface albedo is vital for assessing and understanding land–surface–atmosphere interactions. We developed a method for estimating instantaneous all-sky at-surface shortwave upwelling radiance and albedo over the Tibetan Plateau. The method accounts for the complex interplay of topography and atmospheric interactions and aims to mitigate the occurrence of data gaps. Employing an RTLSR-kernel-driven model, we retrieved surface shortwave albedo with a 1 km resolution, incorporating direct, isotropic diffuse; circumsolar diffuse; and surrounding terrain irradiance into the all-sky solar surface irradiance. The at-surface upwelling radiance and surface shortwave albedo estimates were in satisfactory agreement with ground observations at four stations in the Tibetan Plateau, with RMSE values of 56.5 W/m² and 0.0422, 67.6 W/m² and 0.0545, 98.6 W/m² and 0.0992, and 78.0 98.6 W/m² and 0.639. This comparison indicated an improved accuracy of at-surface upwelling radiance and surface albedo and significantly reduced data gaps. Valid observations increased substantially in comparison to the MCD43A2 data product, with the new method achieving an increase ranging from 40% to 200% at the four stations. Our study demonstrates that by integrating terrain, cloud properties, and radiative transfer modeling, the accuracy and completeness of retrieved surface albedo and radiance in complex terrains can be effectively improved.
... South Africa's "favorable" geographic positioning implies that it may have great potential in using solar energy. Through the development of geodatabases built upon years of remotely sensed data on solar energy, a web-based tool-PVGIS, (version 5.2) presents capabilities of monitoring and assessing the solar potential of different locations [11,14,15]. In this regard, this research attempts to compare and assess the regional potential of solar energy between the two provinces, using data from the PVGIS tool and the Kriging technique, to study how the mentioned provinces could be suited for future solar technologies. ...
South Africa has committed to reducing its greenhouse emissions by sixty-five percent by 2030 in their National Integrated Energy Plan (NEIP). The lack of investment and development for renewable energy sources put the country on an uncertain trajectory in fulfilling its 2030 energy commitments. At the same time, the country has been labeled as a region with one of the highest solar energy potentials. Provinces such as Mpumalanga and Northern Cape are on opposite ends of the matter, with Northern Cape is one of the leading provinces for renewal energy, while the Mpumalanga province remains the host to eighty-five per cent of the country's coal plants. Solar energy is an abundant renewable energy source and can be assessed using Geographic Information Systems (GIS) techniques. In this paper, the geostatistical technique, Kriging, is employed to predict, estimate, and compare the regional distribution, potential, and variability of annual optimum solar energy (irradiance) between the Mpumalanga Province and Northern Cape Province. Spot-based radiation data are available for solar energy analyses from the GIS Web-based tool Photovoltaic Geographical Information Systems (PVGIS). Kriging was used to estimate the spatial variability of solar energy at an average error of 1.98505% for the Northern Cape Province and 2.32625% for the Mpumalanga Province. It was identified that the Northern Cape receives the highest annual optimum irradiation and has a low overall spatial variation in irradiation over its provincial area. Mpumalanga receives lesser amounts of irradiation but has high overall spatial variation over its provincial area. Most of Northern Cape's central to northwestern regions have the highest annual optimum irradiation ranging from 2583 kWh/m 2 to 2638 kWh/m 2 , while Mpumalanga's highest regions of annual irradiation occur primarily on its western and northwestern parts and ranges in highs of 2345 kWh/m 2 to 2583 kWh/m 2 .
... The data record covers the time period from 1983 to near real time with a spatial resolution of 0.05 • × 0.05 • . In order to derive the SARAH-2 surface parameters, the Heliosat algorithm is used (Hammer et al., 2003). It provides a continuous dataset of Effective Cloud Albedo and minimizes the impacts of satellite changes and artificial trends 135 due to degradation of satellite instruments through an integrated self-calibration parameter Mueller et al. (2011). ...
The broad geographical coverage and high temporal and spatial resolution of geostationary satellite data provide an excellent opportunity to collect information on variables whose spatial distribution and temporal variability are not adequately represented by the in situ networks. This study focuses on assessing the effectiveness of two geostationary satellite-based sunshine duration (SDU) datasets over Brazil, given the relevance of SDU to various fields, such as agriculture and energy sectors, to ensure reliable SDU data over the country. The analyzed datasets are the operational products provided by the Satellite Application Facility on Climate Monitoring (CMSAF), that uses data achieved with the Meteorological Satellite (Meteosat) series, and by the Satellite and Meteorological Sensors Divison of the National Institute for Space Research (DISSM/INPE), that employs Geostationary Operational Environmental Satellite (GOES) data. The analyzed period ranges from September 2013 to December 2017. The mean bias error (MBE), mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (r) and scatterplots between satellite products and in situ daily SDU measurements provided by the National Institute of Meteorology (INMET) were used to access the products performance. They were calculated on a monthly basis and grouped into climate regions. The statistical parameters exhibited a uniform spatial distribution, indicating homogeneity within a given region. Except for the Tropical Northeast Oriental (TNO) region, there were no significant seasonal dependencies observed. The Mean Bias Error (MBE) values for both satellite products were generally low across most regions in Brazil, mainly between 0 and 1 hour. The correlation coefficient (r) results indicated a strong agreement between the estimated values and the observed data, with an overall r value exceeding 0.8. Nevertheless, there were notable discrepancies in specific areas. The CMSAF product showed a tendency to overestimate observations in the TNO region, with MBE consistently exceeding 1 hour for all months, while the DISSM product exhibited a negative gradient of MBE values in the west-east direction, in the northern portion of Brazil. The scatterplots for the TNO region revealed that the underestimation pattern observed in the DISSM product was influenced by the sky condition, with more accurate estimations observed under cloudy skies. Additional analysis suggested that the biases observed might be attributed to the misrepresentation of clear-sky reflectance. In the case of the CMSAF product, the overestimation tendency observed in the TNO region appeared to be a result of systematic underestimation of the Effective Cloud Albedo. The findings indicated that both satellite-based SDU products generally exhibited good agreement with the ground observations across Brazil, although their performance varied across different regions and seasons. The analyzed operational satellite products present a reliable source of data to several applications, being an asset due to its high spatial resolution and low time latency.
... Exemplaire réservé à François Hénault 78 Le solaire à concentration faites au sol. Des exemples relatifs à l'estimation de la ressource à l'aide d'images satellitaires peuvent être trouvés dans (Hammer et al. 2003 ;Rigollier et al. 2004 ;Aguiar et al. 2015 ; Alonso-Montesinos et Battles 2015). En parallèle de l'estimation de la ressource, une estimation du déplacement des masses nuageuses permet d'anticiper les variations de l'éclairement au cours des heures à venir (Hamill et Nehrkorn 1993). ...
Les énergies renouvelables font partie intégrante de la stratégie énergétique de la plupart des pays. Ce contexte engage à considérer avec attention toutes les solutions qui peuvent contribuer à l’émergence d’un monde décarboné. Parmi ces solutions, le solaire occupe une position de choix avec ses modes de conversion photovoltaïque et thermique, dont le solaire à concentration.La technologie du solaire à concentration offre une solution originale en complément de l’éolien et du photovoltaïque. Elle présente des solutions de stockage thermique massif à faible coût, des options de conversion hybrides et se décline dans des domaines d’applications connexes à l’électricité comme la chaleur industrielle ou les combustibles de synthèse.Didactique et complet, Le solaire à concentration expose les notions fondamentales, les applications concrètes et les développements futurs de cette ressource prometteuse.
... Irradiances are calculated from meteorological satellite image data using the Heliosat method [21]. Images are provided by EUMETSAT Meteosat Second Generation (MSG) satellites in a temporal resolution of 15 min. ...
Self-consumption of the energy generated by photovoltaics (PV) is playing an increasingly important role in the power grid. “Prosumer” systems consume part of the produced energy directly to meet the local demand, which reduces the feed-in into as well as the demand from the grid. In order to analyse the effects of PV self-consumption in the power grid, we introduce a stochastic bottom-up model of PV power generation and local consumption in the control area of the German transmission system operator TransnetBW. We set up a realistic portfolio of more than 100,000 PV/prosumer systems to generate representative time series of PV generation and consumption as a basis to derive self-consumption and feed-in. This model allows for the investigation of the time-dependent behaviour in detail for the full portfolio whereas measurements are presently only available as aggregated feed-in time series over a nonrepresentative subset of systems. We analyse the variation of self-consumption with PV generation and consumption at the portfolio level and its seasonal, weekly and diurnal cycles. Furthermore, we study a scenario of 100% prosumers as a limiting case for a situation without subsidized feed-in tariffs and local energy storage.
... Based on satellite data, numerous solar analyses are carried out, an example of which is the study of Escobar et al. [24][25][26] and many others. The use of remote sensing technologies in this type of research is also common [27]. These types of studies provide two-dimensional analyses enabling the acquisition of solar maps. ...
UAVs have recently become a very popular tool for acquiring geospatial data. Photographs, films, images, and results of measurements of various sensors from them constitute source material for generating, among other things, photographic documentation, visualisation of places and objects, cartographic materials and 3D models. These models are not only material for the visualisation of objects but are also source material for spatial analysis, including the assessment and analyses of the solar potential of buildings. This research aims to benchmark the feasibility of using UAV-derived data acquired from three sensors, namely the DJI Zenmuse P1 camera, the Share PSDK102S v2 multi-lens camera and the DJI Zenmuse L1 laser scanner. The data from these were acquired for the construction of comprehensive and reliable 3D models, which will form the basis for generating solar potential maps. Various sensors, data storage formats, and geospatial data processing capabilities are analysed to determine the most optimal and efficient solution for providing accurate, complete and reliable 3D models of places and objects for the construction of solar potential maps. In this paper, the authors prepare a compilation of the results of the studies from different measurement combinations and analyse the strengths and weaknesses of the different solutions, as well as the integration of the results for an optimal 3D model, which was used to perform solar potential analyses for the selected built-up area. The results of the study show that the parameters for assessing the quality of a 3D model can be statistical parameters that determine the coplanarity of roof slope points (i.e., standard deviation, distances from the plane, and RMS value). The completeness of the model is defined as the percentage of the recorded area by sensors to the total area of the model.
... In contrast, OF + F yield overestimations in time horizon 15 to 135 min ahead and underestimations in 150 to 180 min ahead. According to the original study, Hammer et al. (Hammer et al., 2003) claimed that the GHI forecast has rRMSE of 45 % in the 180 min time horizons. Meanwhile, Xie et al. (Xie et al., 2016) exhibited FARMS' RMSE of roughly -130.28 ...
Short-term global horizontal irradiance (GHI) forecast methodologies are routinely employed to mitigate the
instability of photovoltaic power and, more importantly, to secure early participation in the energy auction
market. The intra-day forecast model substantially consists of the combination of a cloud motion vector (CMV)
derivation and a GHI extraction model. This study utilized optical flow (OF) method to calculate CMV from two
subsequent satellite images and predict cloud displacement up to 180 min ahead. Meanwhile, the conventional
GHI extraction models (Hammer and the fast all-sky radiation model for solar applications (FARMS) models)
were employed to extract future GHI. Moreover, to the best of our knowledge, only a few studies have been
dedicated to combining OF method with deep learning model. Hence, this study proposed a novel combination
based on OF method and long short-term memory model (LSTM) to enhance temporal horizon and accuracy.
After all combinations of OF method and GHI models were simulated and assessed under all-sky conditions, the
OF method that combined with LSTM outperformed the OF method combined with conventional models across
all time horizons. For root mean square error (RMSE) and forecast skill (FS), the accuracy of OF and LSTM models
for all time horizon ranges from 59.64 W/m^2 to 120.63 W/m^2 and 16.40 % to 54.42 %, respectively. The study
findings are expected to encourage the inclusion of satellite data in GHI forecast as well as the optimization of
energy management systems that integrate with a photovoltaic system
... Introduction Solar energy is one of the most promising sources of renewable energy, with the potential to power homes, businesses, and entire communities with clean and sustainable power [8]. In this section, we will provide a brief history of solar energy and its development over time, explain the basics of solar energy generation, and discuss the benefits of solar energy [9]. ...
"Solar, Heat, and the Grid" is a rapidly growing area of energy research and development that aims to
optimize the use of renewable energy sources and reduce dependence on fossil fuels. The integration of
solar energy, heating systems, and the electrical grid is seen as key to achieving a more sustainable energy
system[1]. Solar panels can generate electricity to power homes and businesses, while also providing hot
water or space heating through solar thermal system[2]s. The use of heat pumps can further increase
energy efficiency and reduce emissions by using electricity to transfer heat from the environment to
homes and buildings[3].
... Consequently, a number of researchers have made efforts to develop effectual methods to evaluate the potential of solar energy with the background of dioxide carbon emission reduction. Remote sensing technology was used to assess solar energy, and the results showed a high spatial resolution and sufficient temporal resolution compared with ground station data [4]. The solar energy in the northern part of Ethiopia was estimated through the primary data; the results indicated that the potential of solar energy resources can be determined [5]. ...
Solar energy is considered one of the most hopeful alternative sources to avoiding dependence on fossil fuels, and it does not cause any air pollution. GIS-based solar energy potential evaluation is mainly focused on regional scale; further, more solar energy potential evaluation with building scale is calculated through observation data and mathematical model. Therefore, in this paper, a GIS-based joint solar energy potential evaluation is developed to evaluate the distributed photovoltaic potential and centralized photovoltaic potential. Shanxi province in China, which has abundant coal resources, is used as the study area. The raster grid scale is used as the minimum research scale, which could not only deal with the distributed photovoltaic potential but could also calculate the centralized photovoltaic potential. The obtained results indicate that the developed method could effectively deal with problems associated with the distributed photovoltaic potential and centralized photovoltaic potential in the raster grid scale.
... First, the method uses the LUT-based Mesoscale Atmospheric Global Irradiance Code (MAGIC) to calculate SSI values for clear sky atmospheres, which was generated previously from the libRadtran Radiative Transfer Model (RTM) [34] under a high number of atmospheric situations and aerosol types [35]. Then, the SSI estimates are extended to cloudy skies by calculating a cloud index that quantifies the cloud percentage over a given area [36]. A clear-sky index is derived from the cloud index to consider the cloud transmission, following the basis of the Heliosat method [29], [37]. ...
Surface Solar Irradiance (SSI) is a crucial component of the radiation budget at the surface, which governs water and energy exchanges with the atmosphere. Good estimates of SSI at regional to global scales are needed for modelling land surface processes, climate and weather predictions, or management of solar power plants. This paper presents the adaptation of the MAGIC-Heliosat method used by the CM-SAF for MSG/SEVIRI to the MTSAT-2/Imager and Himawari-8/AHI sensors managed by the Japanese Meteorological Agency. The method allows providing estimates of Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) over the Asian Pacific coast and Oceania. These estimates were evaluated by comparison to ground data measured at six BSRN stations during years 2014 and 2016. The results showed that GHI can be determined with an accuracy of -5 W m
-2
; a precision of 160 W m
-2
; and a relative absolute error of 30% in an hourly basis. They improved to an accuracy of -5 W m
-2
(-5 W m
-2
), a precision of 70 W m
-2
(40 W m
-2
), and a relative error of 10% (7%) in daily (monthly) estimates. The results for DNI showed an accuracy of -45 W m
-2
and a precision of 330 W m
-2
, which represent a relative absolute error of 38%. These results improved for longer time steps, with an accuracy of +15 W m
-2
(+30 W m
-2
), a precision of 150 W m
-2
(130 W m
-2
), and relative error of 35% (20%) in daily (monthly) estimations.
... We use in this experiment, a set of greyscale satellite images collected by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the geostationary satellite Meteosat Second Generation (MSG) operated by EUMETSAT [12] (Meteosat SEVIRI Rapid Scan image data 1 ). These 128 × 128 pixel images cover a 2. Similarly to Heliosat methods [20,57], a cloud index (or "cloud albedo" [42]) is derived from the calibrated radiance observed by the satellite sensor. Following the method presented in [50], the cloud index (CI) is computed from the statistics of the pixel values with ρ(i, j, t) the value of a given pixel (i, j) at time t; ρ min (i, j, t−N : t) the minimum pixel value of the same pixel (i, j) at the same time t of the day over the last N days (N = 10 here) and ρ max (t) the maximum pixel value in the image at time t (Equation 11). ...
Efficient integration of solar energy into the electricity mix depends on a reliable anticipation of its intermittency. A promising approach to forecasting the temporal variability of solar irradiance resulting from the cloud cover dynamics is based on the analysis of sequences of ground-taken sky images or satellite observations. Despite encouraging results, a recurrent limitation of existing deep learning approaches lies in the ubiquitous tendency of reacting to past observations rather than actively anticipating future events. This leads to a frequent temporal lag and limited ability to predict sudden events. To address this challenge, we introduce ECLIPSE, a spatio-temporal neural network architecture that models cloud motion from sky images to not only predict future irradiance levels and associated uncertainties, but also segmented images, which provide richer information on the local irradiance map. We show that ECLIPSE anticipates critical events and reduces temporal delay while generating visually realistic futures. The model characteristics and properties are investigated with an ablation study and a comparative study on the benefits and different ways to integrate auxiliary data into the modelling. The model predictions are also interpreted through an analysis of the principal spatio-temporal components learned during network training.
... The solar radiation reaching ground level is often called surface solar irradiation (SSI). SSI clearly affects temperature and precipitation, drives large-scale atmospheric circulation, and also plays an essential role in some other processes such as global energy balance [24,29], oceanic heat budget [30], photosynthesis [13], solar energy production [7] and power productivity [33], etc. SSI shows substantial spatial and temporal variability because it depends on the position of the sun and the cloud cover in the sky [18]. Observations in many regions over the globe indicate a decrease SSI from ~ 1950s to 1980s ("dimming"), followed by an increase SSI during 1990s ("brightening") in all-sky conditions [12,23,32,34]. ...
Currently, the application of numerical models in simulating, forecasting, and predicting meteorological factors and features of the atmosphere, including solar radiation combined using satellite data, is widely used. Therefore, comparing the difference between the parameters calculated from the model with the satellite is extremely necessary, thereby evaluating the quality of the model as well as the quality of the satellite products compared to the value of the satellite with monitoring or re-analysis, is the basis for making accurate forecasts/forecasts in the future. This study aims to assess the regional climate model RCA4 (RCP4.5 and RCP8.5) to simulate surface solar irradiation (SSI) based on estimated radiation data from the Himawari-8 satellite for Viet Nam using statistical indicators. The results showed that the radiation values estimated from model correlated well with estimates from the satellite, which has been validated very close to the observation at the surface. In the spring and winter, the general correlation trend shows that the radiation at the stations in the North tend to be higher than the stations in the South of Viet Nam. Based on the assessment of the RCA4 model compared to satellites in the period (2016-2018), the results of model are used to analyze the progress of solar radiation during the year in 7 climate zones of Viet Nam between different versions. It can be seen that the maximum/minimum values of the month do not change much between versions. Comparing to the period 1976-2005, the estimated shortwave radiation at the surface in the period 2020-2050 decreases in most of Viet Nam for both scenarios RCP4.5 and RCP8.5. In contrast, the Central Highlands region shows an increase in shortwave radiation.
Satellite‐based (SAT) methods are widely used to forecast surface solar irradiance up to several hours ahead. Herein, a cloud index‐based version of the Heliosat method is applied to infer irradiance from Meteosat Second Generation images. The cloud index (CI) is derived from images in the visible range and quantifies the impact of clouds on surface solar irradiance. Conventional SAT methods utilize cloud motion vectors (CMVs) from consecutive CI images to predict future cloud conditions and subsequently retrieve irradiance. In this study, HelioNet is introduced—a convolutional neural network (CNN) with UNet architecture designed to predict future CI situations from sequences of preceding CI images. Forecasts of two HelioNet configurations are benchmarked against CMV and persistence over a full year (2023), with lead times (LT) up to 4 h. HelioNet 15 min recursively generates forecasts at 15 min resolution. HelioNet hybrid begins with forecasts at 15 min resolution for , then uses a 45 min resolved model to forecast all remaining LT steps. HelioNet 15 min achieves root mean square error (RMSE) improvements of >15% over the CMV model within the first hour on image level. HelioNet hybrid shows superior performance for all LT across all metrics considered, with an average RMSE improvement of >11% on image and 8% at irradiance level.
Forecasting plays an indispensable role in the grid integration of solar energy, which is an important pathway toward the grand goal of achieving planetary carbon neutrality. This rather specialized field of solar forecasting constitutes both irradiance and photovoltaic power forecasting. Its dependence on atmospheric sciences and implications for power system operations and planning make the multi-disciplinary nature of solar forecasting immediately obvious. Advances in solar forecasting represent a quiet revolution, as the landscape of solar forecasting research and practice has dramatically advanced as compared to just a decade ago.
Solar Irradiance and Photovoltaic Power Forecasting provides the reader with a holistic view of all major aspects of solar forecasting: the philosophy, statistical preliminaries, data and software, base forecasting methods, post-processing techniques, forecast verification tools, irradiance-to-power conversion sequences, and the hierarchical forecasting and firm forecasting frameworks. The book’s scope and subject matter are designed to help anyone entering the field or wishing to stay current in understanding solar forecasting theory and applications. The text provides concrete and honest advice, methodological details and algorithms, and broader perspectives for solar forecasting.
The broad geographical coverage and high temporal and spatial resolution of geostationary satellite data provide an excellent opportunity to collect information on variables whose spatial distribution and temporal variability are not adequately represented by in situ networks. This study focuses on assessing the effectiveness of two geostationary satellite-based sunshine duration (SDU) datasets over Brazil, given the relevance of SDU to various fields, such as agriculture and the energy sector, to ensure reliable SDU data over the country. The analyzed datasets are the operational products provided by the Satellite Application Facility on Climate Monitoring (CMSAF) that uses data achieved with the Meteorological Satellite (Meteosat) series and by the Satellite and Meteorological Sensors Division of the National Institute for Space Research (DISSM–INPE) that employs Geostationary Operational Environmental Satellite (GOES) data. The analyzed period ranges from September 2013 to December 2017. The mean bias error (MBE), mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (r), and scatterplots between satellite products and in situ daily SDU measurements provided by the National Institute of Meteorology (INMET) were used to access the performance of the products. They were calculated on a monthly basis and grouped into climate regions. The statistical parameters exhibited a uniform spatial distribution, indicating homogeneity within a given region. Except for the tropical northeast oriental (TNO) region, there were no significant seasonal dependencies observed. The MBE values for both satellite products were generally low across most regions in Brazil, mainly between 0 and 1 h. The correlation coefficient (r) results indicated a strong agreement between the estimated values and the observed data, with an overall r value exceeding 0.8. Nevertheless, there were notable discrepancies in specific areas. The CMSAF product showed a tendency to overestimate observations in the TNO region, with the MBE consistently exceeding 1 h for all months, while the DISSM product exhibited a negative gradient of the MBE values in the west–east direction in the northern portion of Brazil. The scatterplots for the TNO region revealed that the underestimation pattern observed in the DISSM product was influenced by the sky condition, with more accurate estimations observed under cloudy skies. Additional analysis suggested that the biases observed might be attributed to the misrepresentation of clear-sky reflectance. In the case of the CMSAF product, the overestimation tendency observed in the TNO region appeared to be a result of systematic underestimation of the effective cloud albedo. The findings indicated that both satellite-based SDU products generally exhibited good agreement with the ground observations across Brazil, although their performance varied across different regions and seasons. The analyzed operational satellite products present a reliable source of data to several applications, which is an asset due to its high spatial resolution and low time latency.
Numerous investigations and research projects carried out over the past several years in a wide range of application domains have revealed the potential of IoT (Internet of Things). Solar energy is a renewable source of energy and a sustainable foundation for human civilization; thus, the use of IoT with solar energy-powered devices has definitely been a revolutionary reformation in technology. Researchers have looked into ways to use IoT to change the network structure by recognizing different ecosystem components for intelligent solar-powered city control. Furthermore, countless studies have been made on solar monitoring and solar tracking systems using different IoT technologies in order to target better efficiency, automated control, and monitoring, maximum energy generation etc. The contribution of this study is to make aware everyone about the integration of the renewable energy with the revolution 4.0 which is the major primary topic of the current technology nowadays. Also, the major projects like smart city are being pursued in many developing countries like India and so concepts like IoT are mandatory for these major projects. Also, the solar panel efficiency may be increased and maintenance expenses decreased with the help of the Internet of Things in monitoring and optimizing the panels. As this technology may aid in managing energy usage in real time, solar power can be more consistent and adaptable to fluctuating demand. This article provides a state-of-the-art review of the application of IoT in effective solar energy utilization. The use of IoT in solar energy tracking, power point tracking, energy harvesting, smart lighting system, PV panels, smart irrigation system, solar inverters, etc., is reviewed. Hence, by merging solar power with the Internet of Things, we can provide companies and households with long-term, affordable energy solutions that help encourage responsible expansion and a better future. The outcome of this study reveals that IoT is very much successful in providing smart and efficient solar energy output from countless devices. A vast scope of work and research on IoT applications for smart solar energy utilization still exists in the future.
The pre-monsoon season heavily influences the precipitation amount in Pakistan. When hydrometeorological parameters interact with aerosols from multiple sources, a radiative climatic response is observed. In this study, aerosol optical depth (AOD) space-time dynamics were analyzed in relation to meteorological factors and surface parameters (soil moisture content) during the pre-monsoon season in the years 2002-2019 over Pakistan. Level-3 (L3) monthly datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-Angle Imaging Spectroradiometer (MISR) were used. Tropical Rainfall Measuring Mission (TRMM) derived monthly precipitation, Atmospheric Infrared Sounder (AIRS) derived air temperature, after moist relative humidity (RH) from Modern-Era Retrospective analysis for Research and Applications, Version-2 (MERRA-2), near-surface wind speed, and soil moisture data derived from Global Land Data Assimilation System (GLDAS) were also used on a monthly time scale. For AOD trend analysis, Mann-Kendall (MK) trend test was applied. Moreover, Autoregressive Integrated Moving Average with Explanatory variable (ARIMAX) was applied to observe the actual and predicted AOD trend, as well as test the multicollinearity of AOD with covariates. The periodicities of AOD were analyzed using continuous wavelet transformation (CWT) and the cross relationships of AOD with prevailing covariates on a time-frequency scale were analyzed by wavelet coherence analysis. A high variation of aerosols was observed in the spatiotemporal domain. The MK test showed a decreasing trend in AOD which was most significant in Baluchistan and Punjab, and the overall trend differs between MODIS and MISR datasets. ARIMAX model shows the correlation of AOD with varying meteorological and soil parameters. Wavelet analysis provides the abundance of periodicities in the 2-8 months periodic cycles. The coherency nature of the AOD time series along with other covariates manifests leading and lagging effects in the periodicities. Through this, a notable difference was concluded in space-time patterns between MODIS and MISR datasets. These findings may prove useful for short-term and long-term studies including oscillating features of AOD and covariates.
نفذت هذه الدراسة لتقييم تحضير أغلفة نانوية مكونة من خمس طبقات حضرت بتقنية طبقة فوق طبقة (Layer By Layer -LBL) عن طريق استعمال محلولين هما الجينات الصوديوم والآخر عامل مضاد للاحياء المجهرية هو مستخلص الكركم وتم تقدير قطر الهالة على طبق بتري يحتوي على البكتريا الموجبة أو السالبة لصبغة كرام لتقدير فعالية المستخلص المضادة للأحياء المجهرية وأوضحت النتائج أن تركيز 0.2% من مستخلص الكركم قد أظهر فعالية تثبيطية ضد هذه الاحياء المجهرية كما استخدم المجهر الالكتروني الماسح في الكشف عن سمك الغلاف النانوي المحضرة حيث بلغ سمك الغلاف الكلي المكون من الألجينات والكركم 167.72.نانومتر، بلغ جهد الزيتا لمحلول الألجينات 43.12- ملي فولت على pH =7 ولمستخلص الكركم 29.45 ملي فولت، كانت قيم نفاذية بخار الماء WVP للـ PET المشحونة 29.091 g.m2/24h)) وللـ PET المشحون والمغلف بالالجينات ومستخلص الكركم 43.636 g.m2/24h))، OTRللغلاف النانوي اذ كانت للـ PET المشحونة هي 14.78 ml/m2.day))، وللـ PET المشحون والمغلف بالالجينات ومستخلص الكركم 19.19ml/m2.day) ). صنعت ثلاث معاملات من جبن المونتري، غلفت المعاملة الأولى بالغلاف الشمعي(M1)، والثانية غلفت بالغلاف الجيلاتيني (M2) والثالثـة غلفـت بغـلاف نانوي مكون من الجينات الصوديوم ومستخلص الكركم (M3) أشارت النتائج الى انخفاض ملحوظ في نسبة الرطوبة المفقودة ونسبة الحموضة التسحيحية للمعاملة M3 وتطور في قيم ADV بتقدم مدة الانضاج كما ان وجود مستخلص الكركم في الغلاف النانوي حدد من النمو المايكروبي للمعاملة (M3) مما جعلها متفوقة في الصفات الحسية على معاملتي المقارنة.
In the current study, the utilization of wheat( Triticum Vulgare )germ incorporated with soft cheese was investigated. The antimicrobial efficiency of the wheat germ was examined against a few pathogenic bacteria such as Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus , and Streptococcus sp. The results presented that, there was an occurrence of inhibitory activity by Streptococcus sp. (25 mm, 30mm, and 32 mm) and Staphylococcus aureus (30-35 mm) when treated with wheat germ as the concentration (25%, 50%, and 100%) tested was increased, respectively. The physicochemical properties of the fortified soft cheese treatments [0% (T1), 0.5% (T2) and 1.5% (T3) at (10 ± 2) °C for 0, 7, 14 and 21 days, respectively, such as acidity analysis, pH value, and the moisture content, fat content, protein content, and ash contentwere also studied. The sensory properties indicate that the fortified soft cheese at 0.5% and 1.5%wheat germ were better accepted in comparison with other treatments. Thus, this study has shown that the incorporation of wheat germ into soft cheese could enhance sensory properties such as color, flavor, and palatability, respectively. Hence, the utilization of wheat germ is the potential to improve the texture, functional and nutritional properties of soft cheese.
Conventional power grids dominated by synchronous generators gradually shift to variable renewable-energy-integrated grids. With the growth of building-level photovoltaic (PV) panels and other inverter-based resource (IBR) deployments in recent years, market retailers and distribution operators have had to deal with the additional operational and management challenges posed by unobserved energy flow if its behind-the-meter (BTM) configuration. Also, the intermittent and stochastic nature of IBR-based solar power introduces uncertainty into the net load forecasting of electric distribution systems. Hence, management and control with high uncertainty in load prediction due to unobservable PV is a technical challenge. This article presents deep-learning-based algorithms for BTM PV power generation using a limited number of sensors in a given distribution system and extending to adjacent geographical areas. The proposed BTM PV forecasting method is based on geometric deep learning—a spatiotemporal graph neural network by processing the relationship between data in a non-Euclidean graph structure. The predictions of short-term BTM PV are aggregated with loss estimation at the data aggregation point with the net load forecasting to compute the true load forecasting with superior performance. The developed BTM PV forecasting method significantly improved true load forecasting results, which were validated and analyzed using a collection of actual BTM PV and load measurements in a test distribution feeder.
This study pictures for the first time incoming solar radiation mean evolution in Central Africa, intercomparing 8 gridded products (namely CERES-EBAF, CERES-SYN1deg, TPDC, CMSAF SARAH-2, CMSAF CLARA-A2, CAMS-JADE satellite products, as well as ERA5 reanalysis and WorldClim 2 interpolated measurements) and station-based estimations (FAOCLIM 2) or measurements. At the mean annual scale, all products picture low levels of global horizontal irradiance (GHI) to the west (SW Cameroon to SW Republic of Congo) and higher levels towards the north and south margins of the region. However, GHI levels in the CMSAF products are much higher than in CERES and TPDC. The mean annual cycles of GHI extracted for 6 sub-regions are bimodal, with two maxima during the two rainy seasons (March–May and September–November) and two minima during the two dry seasons (December–February and June–August). These seasonal cycles are well reproduced by most products except their amplitude which is dampened in TPDC. At the daily and sub-daily time-scales, products were compared with in-situ measurements from ten meteorological stations located in the western part of Central Africa. The products' performance is assessed through scores as bias and RMSE but also by considering the diurnal cycles' shape, amplitude and frequency of occurrence along the annual cycle. The products properly reproduce the shape of the four types of diurnal cycles with nonetheless noticeable differences in the cycle's frequencies of occurrence.
Obstacles that cast shading on commercially oper-ated PV power plants can lead to a variety of issues, besides causing less energy, like false alerts in failure detection systems or skewed performace ratios. The detection and monitoring of shading effects using on-site inspections can be challenging, especially when one handles a large portfolio of power plants over a period of many years, since shading behaviours can also change over time. We apply an unsupervised method for detecting shading directly from power measurements to create so called shading masks, which make binary statements over whether or not a power plant or subplant is subject to shading at a given time. The shading masks are compared with the results of on-site inspections and they are used to create loss estimates.
This chapter presents solar resource‐related notions essential for its measurement, assessment or forecasting. Solar radiation reaching the ground depends on the position of the Sun, extra‐terrestrial radiation and the atmospheric specificities of the site being studied. The chapter presents a general presentation of these concepts. A fraction of the extra‐terrestrial irradiance was absorbed while traveling through the atmosphere. Another fraction of this radiation is scattered, leading to an indirect radiation for the observer. The chapter offers a detailed presentation of the direct and indirect components of the irradiance, as well as the associated measurement instruments. It then presents the forecasting of the solar resource used by concentrated solar power plants, namely the direct normal irradiance. After a review of the essential notions to be known, the various existing approaches for making a direct normal solar irradiance forecasting are presented, differentiating them according to the expected spatial–temporal horizon.
Global solar radiation (GSR) prediction plays an essential role in planning, controlling and monitoring solar power systems. However, its stochastic behaviour is a significant challenge in achieving satisfactory prediction results. This study aims to design an innovative hybrid prediction model that integrates a feature selection mechanism using a Slime-Mould algorithm, a Convolutional-Neural-Network (CNN), a Long-Short-Term-Memory Neural Network (LSTM) and a final CNN with Multilayer-Perceptron output (SCLC algorithm hereafter). The proposed model was applied to six solar farms in Queensland (Australia) at daily temporal horizons in six different time steps. The comprehensive benchmarking of the obtained results with those from two Deep-Learning (CNN-LSTM, Deep-Neural-Network) and three Machine-Learning (Artificial-Neural-Network, Random-Forest, Self-Adaptive Differential-Evolutionary Extreme-Learning-Machines) models highlighted a higher performance of the proposed prediction model in all the six selected solar farms. From the results obtained, this work establishes that the designed SCLC algorithm could have a practical utility for applications in renewable and sustainable energy resource management.
In spite of WRF-Solar being a numerical weather prediction (NWP) model specifically tailored for solar energy applications, the model lacks a specific cloud initialization component to improve short-range predictions. To overcome this limitation we have combined the fundamental concepts of the Multi-sensor Advection Diffusion nowCast (MADCast) model of using satellite observations to detect the clouds and advect and diffuse the clouds within a NWP model, with the better cloud–aerosol–radiation physics of WRF-Solar, into a new model referred to as MAD-WRF. The MAD-WRF cloud initialization combines a cloud parameterization that infers the presence of clouds based on relative humidity with observations of the cloud mask and cloud top/base height to provide a three-dimensional cloud analysis (liquid, ice, and snow hydrometeor content). During the forecasts, the hydrometeors can be advected and diffused with no microphysics, in what we refer to as the MAD-WRF passive mode. Alternatively, these passive hydrometeors can be integrated into the explicitly resolved hydrometeors during a nudging phase, designated the MAD-WRF active mode. In the nudging phase, there is a relaxation of the resolved hydrometeors towards the passive hydrometeors. Both modes aim to avoid an abrupt dissipation of clouds if the atmospheric environment is not adequate to support them. Both MAD-WRF active and passive modes as well as WRF-Solar have been run in a demonstration of MAD-WRF over the contiguous U.S., and results from these short-range forecasts (0–6 h) are presented here to illustrate the added value of MAD-WRF for global horizontal irradiance predictions. These results clearly illustrate the added value that MAD-WRF brings for short term irradiance predictions with the WRF-Solar model.
Presents a modification to the parameterization scheme of Stephens which improves on the estimation of shortwave absorption by cloud. In particular, the variation of cloud absorption with solar elevation angle is improved with the modified scheme. -from Authors
We correct an error in a widely used air mass table by recalculating the values on the basis of the ISO Standard Atmosphere (1972) and revise its approximation formula.
We correct an error in a widely used air mass table by recalculating the values on the basis of the ISO Standard Atmosphere (1972) and revise its approximation formula.
A statistical method is presented for the determination of the global solar radiation at ground level. It makes use of data from the meteorological satellites, which provide extensive coverage as well as adequate ground resolution. In the first step, a reference map of ground albedo is deduced from the time-sequence of satellite images. Then, by comparing the satellite data with the computed albedo map, a cloud coverage index is determined for each ground point of 5 km x 5 km. This index is linearly correlated to the atmospheric transmission factor. The regression parameters are estimated using a training set provided by ground pyranometers. Tests for two different time periods show a good agreement between the actual and model-derived hourly global radiation.
Spectral data on extraterrestrial solar radiation, Rayleigh scattering, ozone absorption and absorption by the uniformly mixed gases are critically evaluated and used for computing the integral Rayleigh optical thickness of the clean and dry atmosphere for a given relative optical air mass or solar elevation angle. The results are compared to the corresponding values calculated with the help of the three parameterization formulae. Based on the comparison, the formula of Louche et al., slightly adjusted to the new values, may be recommended for general use. Equations are given for converting the earlier into the new values of the integral Rayleigh optical thickness and of the Linke turbidity factors based thereom. Both the earlier and the new algorithms yield identical values of the direct solar irradiance.
Forecasting of solar irradiance will become a major issue in the future integration of solar energy resources into existing energy supply structures. As far as short-term time horizons (up to 2h) are concerned, satellite data are a high quality source for information about radiation with excellent temporal and spatial resolution. Due to the strong impact of cloudiness on surface irradiance the description of the temporal development of the cloud situation is essential for irradiance forecasting. As a measure of cloudiness, cloud index images according to the Heliosat method are calculated from METEOSAT images. To predict the future cloud index image from a sequence of subsequent images different approaches were applied. A statistical method was used to derive motion vector fields from two consecutive images. The future image then is determined by applying the calculated motion vector field to the actual image. As a completely different approach Neural Networks in combination with Principal Component Analysis were used to describe the development of the cloud structure. The accuracy of the predicted cloud images was analysed and compared for both methods. Motion vector fields showed a superior performance and were chosen for further evaluations. Finally, solar surface irradiance was derived from the predicted cloud index images with the Heliosat method and compared to ground measurements.
A new, simple model for calculating clear-sky direct and diffuse spectral irradiance on horizontal and tilted surfaces is presented. The model is based on previously reported simple algorithms and on comparisons with rigorous radiative transfer calculations and limited outdoor measurements. Equations for direct normal irradiance are outlined; and include: Raleigh scattering; aerosol scattering and absorption; water vapor absorption; and ozone and uniformly mixed gas absorption. Inputs to the model include solar zenith angle, collector tilt angle, atmospheric turbidity, amount of ozone and precipitable water vapor, surface pressure, and ground albedo. The model calculates terrestrial spectra from 0.3 to 4.0 ..mu..m with approximately 10 nm resolution. A major goal of this work is to provide researchers with the capability to calculate spectral irradiance for different atmospheric conditions and different collector geometries using microcomputers. A listing of the computer program is provided.
The paper presents an improved version of a previously published model for the diffuse fraction of hourly global irradiance. In addition to hourly solar elevation and clearness index, an hour-to-hour variability index and regional surface albedo are included among the input parameters. Moreover, to prevent excessively high normal incidence beam irradiances at very low solar elevations, the model does not allow a solar elevation dependent maximum beam transmittance to be exceeded. This new model is tuned to 32 years of data from Bergen, Norway. Moreover, a test against independent data from four European stations showed that the model performs better than the models of Erbs et al. (1982), Maxwell (1987)and Perez et al. (1992).
Estimates of hourly global irradiance based on geostationary satellite data with a resolution of several (2 to 10) kilometres
reproduce ground-measured values with a Root Mean Square Error (RMSE) of typically 20% to 25%. The different components of
this RMSE have been enumerated by several authors but, due to the lack of adequate measurements, their respective importance
is not well settled. In the present study we attempt to quantify these components from a practical point of view, that is
from the point of view of users having to rely on time/site specific irradiance data. We conclude that the intrinsic, or “effective”
RMSE is more along the line of 12%. This effective RMSE is the measure of the methodological imprecision (satellite-to-irradiance
conversion models). The remaining part of the overall RMSE is the amount by which spatially averaged satellite-derived estimates
are, by their very nature, bound to differ from ground-measured local insolation.
Images taken by geostationary satellites may be used to estimate solar irradiance fluxes at the earth's surface. The Heliosat method is a widely applied procedure for this task. It is based on the empirical correlation between a satellite derived cloud index and the irradiance at the ground. Modifications to this procedure that may reduce the temporal variability of the correlation are presented. The modified method may open the way to the use of a generic relation of cloud index and global irradiance.
The luminous efficacy of solar irradiance under cloudless sky is evaluated by a spectral radiative transfer model. Based on model runs with input data from Bergen, Norway, the beam and diffuse luminous efficacies are parameterized for the cloudless case in terms of solar elevation and day number of the year. The luminous efficacy under an unbroken cloud cover is evaluated by a combination of the cloudless model and an overcast radiative transfer model, and then parameterized in terms of solar elevation and clearness index. Based on a physical argument, an overall model is finally presented, yielding the luminous efficacy at arbitrary cloud conditions by a tuned interpolation between the efficacies for the overcast and the cloudless case. This model yields deviations from observations that are small relative to the luminous efficacy variations caused by variations in solar elevation and cloudiness.
Dateiformat: zip, Dateien im PDF-Format. Oldenburg, Universiẗat, Diss., 2000. Computerdatei im Fernzugriff.
A short-wave parameter-ization revised to improve cloud absorption Effective accuracy of satellite-derived hourly irradiances. Theoretical and Applied Climato-logy
- G Stephens
- S Ackerman
- E Smith
- A Zelenka
- R Perez
- R Seals
- D Renne
Stephens, G., Ackerman, S., & Smith, E. (1985). A short-wave parameter-ization revised to improve cloud absorption. Journal of the Atmospheric Sciences, 41, 687–690. Zelenka, A., Perez, R., Seals, R., & Renne, D. (1999). Effective accuracy of satellite-derived hourly irradiances. Theoretical and Applied Climato-logy, 62, 199–207. A. Hammer et al. / Remote Sensing of Environment 86 (2003) 423–432 432
Algorithms for the Satellight programme Use of weather and climate research satellites for estimating solar resources
- J Page
- D S Renne
- R Perez
- A Zelenka
- C Whitlock
- R Dipasquale
Page, J., 1996. Algorithms for the Satellight programme. Tech. rep. Renne, D. S., Perez, R., Zelenka, A., Whitlock, C., & DiPasquale, R. (1999). Use of weather and climate research satellites for estimating solar resources. Advances in Solar Energy, 13.
Short-term forecasting of solar radiation based on image analysis of meteosat data
- A Hammer
- D Heinemann
- E Lorenz
- B Lü
Hammer, A., Heinemann, D., Lorenz, E., & Lü, B. (1999). Short-term forecasting of solar radiation based on image analysis of meteosat data. Proc. EUMETSAT meteorological satellite data users conference ( pp. 331 – 337).
Effect of Meteosat VIS sensor properties on cloud reflectivity
- A Hammer
- D Heinemann
- C Hoyer
Hammer, A., Heinemann, D., & Hoyer, C. (2001). Effect of Meteosat VIS sensor properties on cloud reflectivity. Third SoDa meeting, Berne (CH).
Prediction of air temperatures from solar radiation
- D Dumortier
Dumortier, D., 2002. Prediction of air temperatures from solar radiation. Tech. rep., SoDa-5-2-4, CNRS-ENTPE.
Modelling global and diffuse horizontal irradiances under cloudless skies with different turbidities
- D Dumortier
Dumortier, D., 1995. Modelling global and diffuse horizontal irradiances under cloudless skies with different turbidities. Daylight II, jou2-ct92-0144, final report vol.
SATELLIGHT—processing of METEOSAT data for the production of high quality daylight and solar radiation available on a world wide web internet server
- M Fontoynont
- D Dumortier
- D Heinemann
- A Hammer
- J Olseth
- A Skartveit
- P Ineichen
- C Reise
- J Page
- L Roche
- H G Beyer
- L Wald
Fontoynont, M., Dumortier, D., Heinemann, D., Hammer, A., Olseth, J., Skartveit, A., Ineichen, P., Reise, C., Page, J., Roche, L., Beyer, H. G., & Wald, L. (1997). SATELLIGHT—processing of METEOSAT data for the production of high quality daylight and solar radiation available on a world wide web internet server, mid-term progress report,
Use of weather and climate research satellites for estimating solar resources Advances in Solar Energy
- D S Renne
- R Perez
- A Zelenka
- C Whitlock
- R Dipasquale
Renne, D. S., Perez, R., Zelenka, A., Whitlock, C., & DiPasquale, R.
(1999). Use of weather and climate research satellites for estimating
solar resources. Advances in Solar Energy, 13.
Algorithms for the Satellight programme
- J Page
Page, J., 1996. Algorithms for the Satellight programme. Tech. rep.
Effect of Meteosat VIS sensor properties on cloud reflectivity
- Hammer
Short-term forecasting of solar radiation based on image analysis of meteosat data
- Hammer
Use of weather and climate research satellites for estimating solar resources
- Renne
SATELLIGHT-processing of METEOSAT data for the production of high quality daylight and solar radiation available on a world wide web internet server, mid-term progress report
- M Fontoynont
- D Dumortier
- D Heinemann
- A Hammer
- J Olseth
- A Skartveit
- P Ineichen
- C Reise
- J Page
- L Roche
- H G Beyer
- L Wald