Global Horizontal Irradiance Measurements Research Articles

Recursive copula-augmented space-correlated temporal very-short term spatiotemporal probabilistic forecasting of the clear-sky index

This study introduces a recursive model framework for augmenting separate temporal probabilistic forecasts of multiple correlated time-series with a copula correlation model. The model is applied to the Markov-chain mixture distribution (MCM) model to spatiotemporally forecast minute resolution normalized solar irradiance, c.f. the clear-sky index, derived from radiometer array measurements of Global Horizontal Irradiance (GHI) for 18 geographically adjacent stations at Oahu, Hawaii, USA. The results are evaluated by univariate and multivariate probabilistic forecast metrics in comparison with forecasts from purely temporal MCM, Climatology and the spatiotemporal Multivariate Persistence Ensemble (MuPEn) benchmark. Results show that the Climatology and the MuPEn forecasts are most reliable, while superiority in sharpness, based on Prediction Interval Normalized Average Width (PINAW), depends on forecast horizon. In terms of accuracy, measured with the univariate measure Continuous Ranked Probability Score (CRPS), the MCM model (with and without copula) forecasts are most accurate for the first two steps ahead forecasts, while the Climatology and MuPEn both have superior score for longer horizons. In terms of multivariate scores, the accuracy estimate Energy Score results for the forecasts are similar to the CRPS, while the Variogram Score, which takes into consideration the correlational structure of the multivariate time-series, is significantly improved by the copula-augmented MCM model compared to the univariate MCM model. The MuPEn model generated the lowest Variogram Score among all models. Tests with fewer stations and swapped training and test data gave similar model-to-model relative dynamics with variations in magnitude.

Twenty-Year Climatology of Solar UV and PAR in Cyprus: Integrating Satellite Earth Observations with Radiative Transfer Modeling

In this study, we present comprehensive climatologies of effective ultraviolet (UV) quantities and photosynthetically active radiation (PAR) over Cyprus for the period 2004 to 2023, leveraging the synergy of earth observation (EO) data and radiative transfer model simulations. The EO dataset, encompassing satellite and reanalysis data for aerosols, total ozone column, and water vapor, alongside cloud modification factors, captures the nuanced dynamics of Cyprus’s atmospheric conditions. With a temporal resolution of 15 min and a spatial of 0.05° × 0.05°, these climatologies undergo rigorous validation against established satellite datasets and are further evaluated through comparisons with ground-based global horizontal irradiance measurements provided by the Meteorological Office of Cyprus. This dual-method validation approach not only underscores the models’ accuracy but also highlights its proficiency in capturing intra-daily cloud coverage variations. Our analysis extends to investigating the long-term trends of these solar radiation quantities, examining their interplay with changes in cloud attenuation, aerosol optical depth (AOD), and total ozone column (TOC). Significant decreasing trends in the noon ultraviolet index (UVI), ranging from −2 to −4% per decade, have been found in autumn, especially marked in the island’s northeastern part, mainly originating from the (significant) positive trends in TOC. The significant decreasing trends in TOC, of −2 to −3% per decade, which were found in spring, do not result in correspondingly significant positive trends in the noon UVI since variations in cloudiness and aerosols also have a strong impact on the UVI in this season. The seasonal trends in the day light integral (DLI) were generally not significant. These insights provide a valuable foundation for further studies aimed at developing public health strategies and enhancing agricultural productivity, highlighting the critical importance of accurate and high-resolution climatological data.

CAELUS: Classification of sky conditions from 1-min time series of global solar irradiance using variability indices and dynamic thresholds

Precise sky classification as a function of cloudiness is desirable or necessary in a variety of applications. CAELUS, a novel classification algorithm that relies on various thresholds to separate all possible sky conditions into six classes, is presented here. It uses global horizontal irradiance (GHI) measurements at 1-min resolution, from which a set of four indices is derived to characterize the magnitude and temporal variability of GHI. The algorithm also requires precise estimates of 1-min GHI under hypothetical cloudless conditions, and the solar zenith angle (limited to a maximum of 85°). Using 1-min GHI measurements from 54 BSRN high-quality radiometric stations, which cover all five primary Köppen-Geiger climate classes, CAELUS is used here to classify their sky conditions. The classification results, including the distribution of sky classes and the transitions between consecutive sky classes, are found consistent with the known characteristics of each primary Köppen-Geiger climate. Moreover, in each climate class, the detection of 1-min cloudless situations is found comparable to that provided by two dedicated and state-of-the-art methods—Reno-Hansen and Bright-Sun.

Rooftop Photovoltaic Energy Production Estimations in India Using Remotely Sensed Data and Methods

We investigate the possibility of estimating global horizontal irradiance (GHI) in parallel to photovoltaic (PV) power production in India using a radiative transfer model (RTM) called libRadtran fed with satellite information on the cloud and aerosol conditions. For the assessment of PV energy production, we exploited one year’s (January–December 2018) ground-based real-time measurements of solar irradiation GHI via silicon irradiance sensors (Si sensor), along with cloud optical thickness (COT). The data used in this method was taken from two different sources, which are EUMETSAT’s Meteosat Second Generation (MSG) and aerosol optical depth (AOD) from Copernicus Atmospheric Monitoring Services (CAMS). The COT and AOD are used as the main input parameters to the RTM along with other ones (such as solar zenith angle, Ångström exponent, single scattering albedo, etc.) in order to simulate the GHI under all sky, clear (no clouds), and clear-clean (no clouds and no aerosols) conditions. This enabled us to quantify the cloud modification factor (CMF) and aerosol modification factor (AMF), respectively. Subsequently, the whole simulation is compared with the actual recorded data at four solar power plants, i.e., Kazaria Thanagazi, Kazaria Ceramics, Chopanki, and Bhiwadi in the Alwar district of Rajasthan state, India. The maximum monthly average attenuation due to the clouds and aerosols are 24.4% and 11.3%, respectively. The energy and economic impact of clouds and aerosols are presented in terms of energy loss (EL) and financial loss (FL). We found that the maximum EL in the year 2018 due to clouds and aerosols were 458 kWh m−2 and 230 kWh m−2, respectively, observed at Thanagazi location. The results of this study highlight the capabilities of Earth observations (EO), in terms not only of accuracy but also resolution, in precise quantification of atmospheric effect parameters. Simulations of PV energy production using EO data and techniques are therefore useful for real-time estimates of PV energy outputs and can improve energy management and production inspection. Success in such important venture, energy management, and production inspections will become much easier and more effective.

Irradiance Nowcasting by Means of Deep-Learning Analysis of Infrared Images

This work proposes and evaluates a method for the nowcasting of solar irradiance variability in multiple time horizons, namely 5, 10, and 15 min ahead. The method is based on a Convolutional Neural Network structure that exploits infrared sky images acquired through an All-Sky Imager to estimate the range of possible values that the Clear-Sky Index will possibly assume over a selected forecast horizon. All data available, from the infrared images to the measurements of Global Horizontal Irradiance (necessary in order to compute Clear-Sky Index), are acquired at SolarTechLAB in Politecnico di Milano. The proposed method demonstrated a discrete performance level, with an accuracy peak for the 5 min time horizon, where about 65% of the available samples are attributed to the correct range of Clear-Sky Index values.

Uncertainty Calculation Method for Photodiode Pyranometers

For all measurements, a provision of uncertainty for each measurement point is good practice. So far, a comprehensive methodology for uncertainties for rotating shadowband irradiometers (RSIs) has not been established, as the typical methods for calculating uncertainty are oversimplified, too time‐consuming, or both when the instrument exhibits significant spectral dependence. The method presented herein allows the estimation of the uncertainty for each measurement provided by the RSIs. The methodology is tested on data from different climates, producing a comprehensive study focused mainly on measurements made with RSIs using photodiode pyranometers. The calculated expanded uncertainty for the RSI measurements matches the deviation from colocated thermopile sensors 95% of the time. Common uncertainty estimations of RSIs from the literature are confirmed using the comprehensive methodology in this article for the deviation and uncertainty calculations. The method is therefore well suited for the application in real measurement campaigns. In this study, photodiode pyranometers are also studied and the methodology for the RSI measurements also works for global horizontal irradiance measurements from photodiode pyranometers.

MPF-Net: A computational multi-regional solar power forecasting framework

Short-term solar irradiance forecasting plays a pivotal role in the effective integration of significantly fluctuating solar power into power grids. Existing computational approaches lack to investigate which climate parameter/s influence the most in attaining the optimal forecasting performance. The paper in hand utilizes diverse feature selection approaches to find the optimal subset of features. Using selected subset of features, a rigorous experimentation is performed with 12 adopted machine learning and 10 newly developed deep learning based regressors for most reliable global horizontal irradiance measurements of 9 different regions of Pakistan using 4 evaluation measures. Further, to attain better predictive performance of solar irradiance, we reap the benefits of different individual regressors and present a robust multi regional meta-regressor. Among machine and deep learning based regressors, proposed meta-regressor along with optimal subset of feature/s achieves the best R2 score of 98% for 6 regions and 97% for other 3 regions of Pakistan. MPF-Net as web service is accessible here.

Estimate of Soiling Rates Based on Soiling Monitoring Station and PV System Data: Case Study for Equatorial-Climate Brazil

Soiling, particulate accumulation on photovoltaic (PV) module surfaces, reduces the available solar resource and the resulting generated device power. In regions where the installation of PV technology is advantageous because of high irradiance areas, in many cases, also possess arid or desert climates that inherently exhibit greater surface soiling. Because of the energy production and associated maintenance-cost factors, it is advantageous to evaluate environmental factors (meteorological variables, climate, and geographical features) as early as possible in developing a PV installation. Alternatively, the determination of the soiling rates (SRate), even after system commissioning can aid in the optimization of maintenance and cleaning-cycle requirements. Significant research has been reported addressing these critical factors through determining local soiling losses. This article presents, evaluates, and compares methodologies to accurately determine and compare SRate and losses from soiling monitoring stations and installed PV systems operating in Belo Horizonte, Brazil (latitude-19.92°, longitude-43.99°). Specifically, data collected from soiling stations were used to estimate the soiling ratio (SRatio) and SRate. A related process is applied to the PV system, using the performance ratio. The methodology incorporates several key refinements. These include normalization and adjustment for the meteorological parameters (temperature and irradiance). Additionally, the differences between the measured global horizontal irradiance (GHI), the plane-of-array irradiance (GPOA) obtained through the application of a transposition model, and the plane-of-array measured by reference cell (Gref) are presented and compared. The results demonstrate that the transposition model is an acceptable option for cases in which only GHI measurements are available.

Detection of clear sky instants from high frequencies pyranometric measurements of global horizontal irradiance

Solar resource assessment by clear sky models is of great importance in the solar energy field: verifying the performance of photovoltaic systems during stable conditions, clouds effects evaluation, the determination of geographical areas where irradiation is more uncertain and the preparation of forecasts with sky cameras. But before using these models they must be validated against high performances soil measurements. Since there is no radiometric sensor that measures clear-sky radiation, then historical clear-sky time periods must be identified only from long-term allsky irradiation records. The contribution of this study is to exploit the ground measurements, analyze them and retrieve the information they contain concerning the clear sky instants. The study will be performed by comparing the clear sky instants identified by an algorithm proposed by Reno and Hansen with a physical clear sky model. This comparison is made using high frequency global horizontal irradiation (GHI) data from high performances meteorological station installed at Benguerir in Morocco.

Detection of clear sky instants from high frequencies pyranometric measurements of global horizontal irradiance

Solar resource assessment by clear sky models is of great importance in the solar energy field: verifying the performance of photovoltaic systems during stable conditions, clouds effects evaluation, the determination of geographical areas where irradiation is more uncertain and the preparation of forecasts with sky cameras. But before using these models they must be validated against high performances soil measurements. Since there is no radiometric sensor that measures clear-sky radiation, then historical clear-sky time periods must be identified only from long-term all-sky irradiation records. The contribution of this study is to exploit the ground measurements, analyze them and retrieve the information they contain concerning the clear sky instants. The study will be performed by comparing the clear sky instants identified by an algorithm proposed by Reno and Hansen with a physical clear sky model. This comparison is made using high frequency global horizontal irradiation (GHI) data from high performances meteorological station installed at Benguerir in Morocco.

BQC: A free web service to quality control solar irradiance measurements across Europe

Classical quality control (QC) methods of solar irradiance apply easy-to-implement physical or statistical limits that are incapable of detecting low-magnitude measuring errors due to the large width of the intervals. We previously presented the bias-based quality control (BQC), a novel method that flags samples in which the bias of several independent gridded datasets is larger for consecutive days than the historical value. The BQC was previously validated at 313 European and 732 Spanish stations finding multiple low-magnitude errors (e.g., shadows, soiling) not detected by classical QC methods. However, the need for gridded datasets, and ground measurements to characterize the bias, was hindering the BQC implementation.To solve this issue, we present a free web service, www.bqcmethod.com, that implements the BQC algorithm incorporating both the gridded datasets and the reference stations required to use the BQC across Europe from 1983 to 2018. Users only have to upload a CSV file with the global horizontal irradiance measurements to be analyzed. Compared to previous BQC versions, gridded products have been upgraded to SARAH-2, CLARA-A2, ERA5, and the spatial coverage has been extended to all of Europe. The web service provides a flexible environment that allows users to tune the BQC parameters and upload ancillary rain data that help in finding the causes of the errors. Besides, the outputs cover not only the visual and numerical QC flags but also daily and hourly estimations from the gridded datasets, facilitating the access to raster data.

An Approach for Data Treatment of Solar Photovoltaic Generation

A good solar power photovoltaic generation forecast depends on good quality time series data from measurements of Global Horizontal Irradiance and Solar Power Generation. However, measurement system failures and errors in data handling can corrupt data records with gaps and outliers that undermine forecasting accuracy. Therefore, it is important that the fitting of solar energy prediction models must be preceded by a data analysis in order to detect and correct measurement errors. Given that Global Horizontal Irradiance and Solar Power Generation are correlated variables, this paper aims to present the main characteristics of an offline approach developed for the joint treatment of hourly data values of both variables in a photovoltaic plant. In the proposed methodology, the measurements of Global Horizontal Irradiance and Solar Power Generation are analyzed by using reanalysis data and statistical and data mining techniques for the correction of outliers and the filling of data gaps. The application of the approach is illustrated by the analysis of measurements from a real solar PV system.

Multi-Horizon Forecasting of Global Horizontal Irradiance Using Online Gaussian Process Regression: A Kernel Study

In the present paper, global horizontal irradiance (GHI) is modelled and forecasted at time horizons ranging from 30 min to 48 h, thus covering intrahour, intraday and intraweek cases, using online Gaussian process regression (OGPR) and online sparse Gaussian process regression (OSGPR). The covariance function, also known as the kernel, is a key element that deeply influences forecasting accuracy. As a consequence, a comparative study of OGPR and OSGPR models based on simple kernels or combined kernels defined as sums or products of simple kernels has been carried out. The classic persistence model is included in the comparative study. Thanks to two datasets composed of GHI measurements (45 days), we have been able to show that OGPR models based on quasiperiodic kernels outperform the persistence model as well as OGPR models based on simple kernels, including the squared exponential kernel, which is widely used for GHI forecasting. Indeed, although all OGPR models give good results when the forecast horizon is short-term, when the horizon increases, the superiority of quasiperiodic kernels becomes apparent. A simple online sparse GPR (OSGPR) approach has also been assessed. This approach gives less precise results than standard GPR, but the training computation time is decreased to a great extent. Even though the lack of data hinders the training process, the results still show the superiority of GPR models based on quasiperiodic kernels for GHI forecasting.

Evaluation of Solar radiation resources and modeling of flexible thin-film modules in Eastern Province – KSA

This paper epitomizes the analysis of the solar resource measurements and a simulation of curved thin-film modules for photovoltaic applications. The analysis used 1 year (January 1, 2018 until December 31, 2018) of data from 7 stations distributed across the eastern province (KSA) based on measurements of Global Horizontal Irradiance (GHI), Diffuse Horizontal Irradiance (DHI), Direct Normal Irradiance (DNI), and related meteorological parameters. The 7 stations show the results obtained for the annual average daily GHI over the study period shows a variation of almost 300 Wh/m2 among station, with mean daily total values in the eastern region 6755 Wh/m2. The results above indicate that photovoltaic would perform well at any location although extreme high temperatures in some locations may degrade the performance of some types of photovoltaic. Annual average daily DNI all above 5500 Wh/m2. The average daily total DHI over the study period among stations is 2840 Wh/m2. Second part of study uses two-step model, 1D and 3D model. The 1D describes the technology and outputs device (current density, temperature, illumination), 3D model uses this data as one of its inputs, and describes size, shape within the curved flexible module.

A Physics-Based DNI Model Assessing All-Sky Circumsolar Radiation.

SummaryBy investigating the long-term observations at Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP), we find that the routinely used Beer-Bouguer-Lambert law and the models that empirically separate direct normal irradiance (DNI) from measurements of global horizontal irradiance (GHI) have dramatic and unexpected bias in computing cloudy-sky DNI. This bias has led to tremendous uncertainty in estimating the electricity generation by solar energy conversion systems. To effectively reduce the bias, this study proposes a physical solution of all-sky DNI that computes solar radiation in the infinite-narrow beam along the sun direction and the scattered radiation falls within the circumsolar region. In sharp contrast with the other DNI models, this method uses a finite-surface integration algorithm that computes solar radiation in differential solid angles and efficiently infers its contribution to a surface perpendicular to the sun direction. The new model substantially reduces the uncertainty in DNI by a factor of 2–7.

Global horizontal irradiance forecast for Finland based on geostationary weather satellite data

Global horizontal irradiance (GHI) estimates and forecasts are necessary for the efficient use of a naturally fluctuating energy source like solar energy. However, few forecasting methods exist for high latitudes. In this study we present the development and validation of a satellite-based GHI forecast for southern Finland, called Solis-Heliosat. The forecast is formed by combining information from the clear sky (CS) model Pvlib Solis with consecutive geostationary weather satellite imagery, using the Heliosat method. Forecasts are produced up to 4 h with a 15-min temporal resolution. The CS model, the satellite-based all-sky estimate, and the GHI forecast have been compared and validated against other methods and in situ GHI measurements. An additional comparison was made for two datasets representing a changing cloud environment.The CS model estimates had an average rMBE (relative Mean Bias Error) of −6% to 1% and a rRMSE (relative Root Mean Square Error) of 6–10%. For the all-sky estimates the rMBE was −4% to −2%, and rRMSE 2–33%. With increasing forecast time the Solis-Heliosat rMBE descends to −9% and rRMSE reaches 50% at 4 h. Solis-Heliosat performs better than the persistence forecasts in most cases, particularly in a changing cloud environment.Our study indicates the use of satellite-based forecasts as a viable tool for forecasting GHI for the solar energy industry also in the high latitudes. In high latitudes geostationary satellite-based methods are at their limit; however, the information they can provide will enable efficient solar energy production.

Evaluation of enhancement events of global horizontal irradiance due to clouds at Patras, South-West Greece

Cloudiness is the dominant factor that affects the transfer of solar irradiance through the atmosphere. Especially the type of clouds, their high spatial and temporal variability, velocity and position relatively to the Sun, could cause events ranging from total reduction to substantial enhancements of surface solar irradiance. In study, the enhancements of Global Horizontal Irradiance (GHI) at South Greece for 2 years are examined. We use 1–min averages of GHI, sunshine duration and synchronized estimations of cloud coverage and type from a digital imaging system. A radiative transfer model is used to estimate GHI under clear skies. For solar zenith angles lower than 80°, 4% of total GHI measurements are considered as enhancements. It is shown that the effect of thick clouds on the GHI enhancements is significantly higher when compared to the thin ones. We also examine cases that correspond to instantaneous clearness index values higher than 1 (Kt > 1): 228 extreme enhancement events are found including 5 cases with GHI values higher than 1367 W/m2 and a maximum value of 1420 W/m2. These results suggest that such events are common and should be included in the design and simulation of the performance of solar energy systems.

Solar irradiance estimations for modeling the variability of photovoltaic generation and assessing violations of grid constraints: A comparison between satellite and pyranometers measurements with load flow simulations

Global horizontal irradiance (GHI) is typically used to model the power output of distributed photovoltaic (PV) generation. On the one hand, satellite estimations are nonpervasive and already available from commercial providers, but they have a limited spatiotemporal resolution. On the other hand, local estimations, e.g., from pyranometers, sky-cameras, and monitored PV plants, capture local irradiance patterns and dynamics, but they require in situ monitoring infrastructure and upgrading the asset of electrical operators. Considering that in most power systems, PV generation is typically the aggregated contribution of many distributed plants, are local GHI estimations necessary to characterize the variability of the power flow at the grid connection point (GCP) and detect violations of the limits of voltages and line currents accurately? To reply, we consider GHI measurements from a dense network of pyranometers (used to model the ground truth GHI potential), satellite estimations for the same area, and information about a medium and low voltage distribution system. We perform load flows at different levels of installed PV capacity and compare the nodal voltages, line currents, and the power at the GCP when the irradiance is from pyranometers and when from satellite estimations, deriving conclusions on the necessity, or not, of highly spatiotemporally resolved irradiance estimations.

Post-processing of NWP forecasts using ground or satellite-derived data through kernel conditional density estimation

Global horizontal irradiance (GHI) forecasts by numerical weather prediction (NWP) often contain model-led bias. There is thus a strong consensus on using post-processing techniques, such as model output statistics (MOS), to correct such errors. As opposed to the conventional parametric methods, this article considers a nonparametric approach for post-processing, namely, kernel conditional density estimation (KCDE). Essentially, KCDE constructs a relationship between the bias error (difference between the NWP-based GHI forecast and measurement) and NWP output variables, such as clear-sky index, zenith angle, air temperature, humidity, or surface pressure. Hence, when a new set of explanatory variables becomes available, the conditional expectation of the bias error can be estimated. Since the ground-based GHI measurements are not available everywhere, the possibility of using satellite-derived GHI data to correct NWP forecasts is also explored. In the case study, two years of GHI forecasts made using the North American Mesoscale forecast system are corrected using both ground-measured and satellite-derived GHI references. As compared to Lorenz's fourth-degree polynomial MOS, additional 10%–16% (using ground-measured GHI) and 5%–13% (using satellite-derived GHI) reductions in the forecast error are observed at 7 test stations across the continental United States.

Characterizing Variability of Solar Irradiance in San Antonio, Texas Using Satellite Observations of Cloudiness

Since the main attenuation of solar irradiance reaching the earth’s surface is due to clouds, it has been hypothesized that global horizontal irradiance attenuation and its temporal variability at a given location could be characterized simply by cloud properties at that location. This hypothesis is tested using global horizontal irradiance measurements at two stations in San Antonio, Texas, and satellite estimates of cloud types and cloud layers from the Geostationary Operational Environmental Satellite (GOES) Surface and Insolation Product. A modified version of an existing solar attenuation variability index, albeit having a better physical foundation, is used. The analysis is conducted for different cloud conditions and solar elevations. It is found that under cloudy-sky conditions, there is less attenuation under water clouds than those under opaque ice clouds (optically thick ice clouds) and multilayered clouds. For cloud layers, less attenuation was found for the low/mid layers than for the high layer. Cloud enhancement occurs more frequently for water clouds and less frequently for mixed phase and cirrus clouds and it occurs with similar frequency at all three levels. The temporal variability of solar attenuation is found to decrease with an increasing temporal sampling interval and to be largest for water clouds and smallest for multilayered and partly cloudy conditions. This work presents a first step towards estimating solar energy potential in the San Antonio area indirectly using available estimates of cloudiness from GOES satellites.