Irradiance Measurements Research Articles

New traceability chain for spectral irradiance measurement at LNE-Cnam

LNE-Cnam has changed its traceability chain for measuring the spectral irradiance of a light source. This facility allows calibration of spectral irradiance of a standard lamp based on a comparison with a high temperature blackbody (HTBB). The reference spectral irradiance is determined by measuring the temperature of the HTBB with a filter radiometer calibrated against our radiant flux reference. This traceability scheme differs from the one used in our former setup, which was mainly based on the International Temperature Scale, ITS-90. Both principles are used in National Measurement Institutes. Our facility uses well known methods adapted to our best capabilities as well as a particular development in optical arrangement and filter radiometer calibration. Thanks to this new measurement setup, our spectral irradiance reference is now traceable to radiometric reference, i.e. our cryogenic radiometer. We have extended our measurement capability to cover the spectral range from 250 nm to 2500 nm. We have simplified the process by reducing the number of benches (three in one) and the number of operations, and we have designed a compact measuring setup through the use of a rotating integrative sphere. This allows us to reduce by at least a factor two our measurement uncertainties over almost the entire spectral range. With this new measurement facility, France participates in the ongoing CCPR k1.a key comparison. This is a key comparison of the Consultative Committee of Photometry and Radiometry for Spectral irradiance from 250 nm to 2500 nm of tungsten halogen lamps. This communication shows the method used, and its validation.

Estimation of solar energy potential over Mongolia based on satellite data

Abstract —We aim to estimate solar resource of Mongolia using satellite data in combination with limited ground measurements. Visible channel images provided by Japanese Geostationary Meteorological Satellite (GMS) Himawari 8 are correlated with ground-based measurements of solar irradiation to derive parameters of the semi-physical model.

Spatio-temporal characterization of long-term solar resource using spatial functional data analysis: Understanding the variability and complementarity of global horizontal irradiance in Ecuador

Understanding the spatio-temporal variability of the solar resource is crucial to effectively support solar power utilization. Unfortunately, long-term and high-resolved measurements of solar irradiance are generally scarce, challenging the characterization for larger areas. In this paper, we propose a methodology to characterize the spatio-temporal variability of global horizontal irradiance (GHI) at a regional scale using long-term satellite-derived data. Spatial functional data analysis (sFDA) is used to identify areas with similar intra-annual variability patterns. The methodology is applied to a 21-year period data on Ecuador retrieved from the National Solar Radiation Database. Being the first time that sFDA is used for this purpose, the results indicate that it provides an appropriate basis for the interannual variability and complementarity analyses. In Ecuador's mainland, twenty-two subregions with four seasonal patterns are identified. The highest GHI potential (5.4 kWhm−2d−1) with the lowest variability (3.4%) is found in the Inter-Andean valleys. Further, seasonal complementarities between the coast and western Andes are identified. In Galapagos, high values are found over all islands (≥4.8 kWhm−2d−1), characterized by three subregions with one seasonal pattern. Our findings provide the first comprehensive spatio-temporal characterization of GHI in Ecuador, which aims at supporting a sustainable energy transition in the country.

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.

Assessing daylight performance in use: A comparison between long-term daylight measurements and simulations

Climate-Based Daylight Modelling (CBDM) methods have been validated against long-term measurements in laboratory settings and found to exhibit errors small enough to make such assessments useful for daylight performance prediction. However, real occupied spaces are affected by a higher number of uncertainties than laboratory or controlled conditions. This study aims at validating CBDM methods against measurements collected in an occupied classroom space, where a monitoring system based on High Dynamic Range Imaging was installed. Four vertical regions were identified on two of the room’s walls, and mean illuminance was calculated for these regions at every time step, both from HDR images and from simulated results. Two simulation methods were evaluated: the 2-phase and the 4-component methods. Sun and sky conditions for the simulations were derived from simultaneous monitored irradiation measurements. Both simulation methods led to moderate over-prediction of HDR-derived results, when considering instantaneous illuminance means and when looking at long-term metrics (cumulative irradiation and Useful Daylight Illuminance). Wall regions exposed to more direct sky- and sunlight were characterised by smaller systematic errors (rMBE = 4%) but similar variance (r2 = 0.83) than regions situated at the back of the room (rMBE = 17–34% and rMAE = 27–37%). Further studies are needed to identify and separate the sources of such errors.

The Origins of Long‐Term Variability in Martian Upper Atmospheric Densities

AbstractWe quantify and interpret the long‐term variability of dayside Martian upper thermosphere and lower exosphere densities within 180–275 km altitudes. Atmospheric CO2, N2, O, and Ar densities are from NASA Mars Atmosphere and Volatile EvolutioN (MAVEN) observations during the time period of 2015–2020 near solar minimum. These neutral measurements, together with contemporaneous solar irradiance measurements at Mars, enable disentanglement of the orbital effect (due to the annual Sun‐Mars distance change with solar longitude) and the solar extreme ultraviolet (EUV) effect in atmospheric density variations. The relative importance of these two effects, which is obtained using a statistical method of Dominance Analysis, reveals the competition between the indirect effect of solar infrared (via the upward coupling from the middle atmosphere) and the direct effect of solar EUV (due to local heating). Our results show that, unlike the orbital effect which is relatively constant at low altitudes and then decreases with increasing altitude, the solar EUV effect nearly monotonically increases. These two effects are comparable at high altitudes (about 240/270/205 km for CO2/N2/O). This analysis is extended to include long‐term exospheric mass density estimates near 400 km from Mars Global Surveyor and Mars Odyssey data, with a focus on representative solar cycle phases of solar minimum and maximum. It is found that near 400 km, the orbital effect is always a key driver regardless of the solar cycle phase, while the solar EUV effect plays a minor role during solar minimum and is greatly enhanced and slightly exceeds the orbital effect during solar maximum.

Ground-based and Satellite Measurements of Solar Spectral Irradiances at 305 nm and 380 nm at a Tropical Site.

The solar radiation has been monitored through ground-based and satellite instruments all over the world for decades. This is also important for both checking and validation of satellite probing. In this work, we compare spectral irradiances at 305 nm (UV-B) and 380 nm (UV-A) from a ground-based radiometer and the Ozone monitoring instrument (OMI) for a tropical site in 2019-2020. Measurements had the auxiliary support of a ground-based imager to identify cloud cover. The presence of clouds introduces the largest differences between satellite and ground-based measurements. In fact, on average, for all-sky (AS, only cloudy skies) conditions such differences (satellite - ground-based) were 46% and 30% at 305 nm and 380 nm, respectively, while for cloud-free-sky (CFS) conditions, the differences dropped to 17% and 8%. In addition, the linear fitting between ground-based and satellite measurements yielded a coefficient of determination (r2 ) of 0.857 (for AS) and 0.984 (for CFS) at 305 nm and 0.774 (for AS) and 0.950 (for CFS) at 380 nm. The differences between these AS and CFS values of r2 were 95% statistically significant. Such results imply the hindrance clouds (and also aerosols based on the results for CFS) still set to obtain UV-B and UV-A surface irradiance from satellite probing.

Modelling and forecasting for solar irradiance from solarimetric station

This paper proposes two approaches for modeling solar irradiance time series. The first is an exploratory analysis and second is a periodic forecast model for solar irradiance data from solarimetric station in a country city of Bahia, Brazil. Two normality hypothesis tests were applied, Anderson-Darling and Shapiro-Wilk. The shape and symmetry of the data were also analyzed using boxplot and histograms to investigate the extreme points, as well as great asymmetry in the distribution of data at different times. Through adherence tests, the normal distribution of solar irradiance in most hours within the annual periodic is rejected. A study by season was carried out, which showed a different behavior in relation to the symmetry of the data, with autumn being the most uniform and spring being the most stochastic. Spring and summer presented better conditions for installation of photovoltaic plates, due to the high solar irradiance rates, and autumn and winter presented satisfactory solar irradiance to maintain this form of generation throughout the year.The second approach is a forecast to solar irradiance in photovoltaic generation systems. The importance of this forecast type is to favor the routine of planning, operation and maintenance of these types of systems, in addition to serving as a basis for feasibility studies and expansion of solar generation. Given a stochastic resource and the periodic behavior of solar irradiance data, a periodic autoregressive model was considered; the statistics were used by the maximum likelihood method, based on hourly measurements of irradiance over a period of one year.

X-ray irradiation measurements of the radiation tolerance of the ITkPixV1 ATLAS pixel readout chip

The ITkPixV1 chip is the pre-production pixel readout chip for the Phase-2 Upgrade of the ATLAS experiment at the HL-LHC. The harsh environment of HL-LHC, including a peak luminosity of 5 × 1034 cm−1 s−1 and an estimated total ionising dose (TID) of around 1 Grad throughout its lifetime is placing strong requirements on the radiation tolerance of the chip. The radiation tolerance of the ITkPixV1 chip has been tested in irradiation campaigns using X-rays. The digital and analog front-end have been tested up to total doses of 1 Grad (at a dose rate of 4 Mrad/h) and show no significant operational issues.

Statistical analysis of multi‐day solar irradiance using a threshold time series model

AbstractThe analysis of solar irradiance has important applications in predicting solar energy production from solar power plants. Although the sun provides every day more energy than we need, the variability caused by environmental conditions affects electricity production. Recently, new statistical models have been proposed to provide stochastic simulations of high‐resolution data to downscale and forecast solar irradiance measurements. Most of the existing models are linear and highly depend on normality assumptions. However, solar irradiance shows strong nonlinearity and is only measured during the day time. Thus, we propose a new multi‐day threshold autoregressive model to quantify the variability of the daily irradiance time series. We establish the sufficient conditions for our model to be stationary, and we develop an inferential procedure to estimate the model parameters. When we apply our model to study the statistical properties of observed irradiance data in Guadeloupe island group, a French overseas region located in the Southern Caribbean Sea, we are able to characterize two states of the irradiance series. These states represent the clear‐sky and non‐clear sky regimes. Using our model, we are able to simulate irradiance series that behave similarly to the real data in mean and variability, and more accurate forecasts compared to linear models.

Optimal Allocation of a Hybrid Photovoltaic Biogas Energy System Using Multi-Objective Feasibility Enhanced Particle Swarm Algorithm

This paper aims to investigate a hybrid photovoltaic (PV) biogas on-grid energy system in Al-Ghabawi territory, Amman, Jordan. The system is accomplished by assessing the system’s reliability and economic viability. Realistic hourly measurements of solar irradiance, ambient temperature, municipal solid waste, and load demand in 2020 were obtained from Jordanian governmental entities. This helps in investigating the proposed system on a real megawatt-scale retrofitting power system. Three case scenarios were performed: loss of power supply probability (LPSP) with total net present cost (TNPC), LPSP with an annualized cost of the system (ACS), and TNPC with the index of reliability (IR). Pareto frontiers were obtained using multi-objective feasibility enhanced particle swarm optimization (MOFEPSO) algorithm. The system’s decision variables were the number of PV panels (Npv) and the number of biogas plant working hours per day (tbiogas). Moreover, three non-dominant Pareto frontier solutions are discussed, including reliable, affordable, and best solutions obtained by fuzzy logic. Double-diode (DD) solar PV model was implemented to obtain an accurate sizing of the proposed system. For instance, the best solution of the third case is held at TNPC of 64.504 million USD/yr and IR of 96.048%. These findings were revealed at 33,459 panels and 12.498 h/day. Further, system emissions for each scenario have been tested. Finally, decision makers are invited to adopt to the findings and energy management strategy of this paper to find reliable and cost-effective best solutions.

Power Ramp-Rate Control via power regulation for storageless grid-connected photovoltaic systems

Photovoltaic Power Ramp-Rate Control (PRRC) constitutes a key ancillary service for future power systems. Although its implementation through the installation of storage systems or irradiance sensors has been widely investigated, fewer studies have explored the power curtailment approach. The latter lacks efficiency, as it voluntarily produces power discharges, yet it is a cost-effective solution in terms of capital expenditures. This paper proposes a novel storageless and sensorless photovoltaic PRRC for grid-connected applications in which the photovoltaic power, rather than the voltage, is the controlled magnitude. The aforementioned contribution makes the effective tracking of the power ramp-rate limit possible compared to the existing methods in the literature. The method is assisted by a real-time curve-fitting algorithm that estimates the Maximum Power Point while operating suboptimally. Thus, no direct temperature or irradiance measurement systems are needed. The validation of the proposed PRRC strategy has been tested by simulation and compared to another approach available in the literature, considering real-field highly variable irradiance data. Experimental validation of the proposed strategy has been performed in real time via Controller Hardware-in-the-Loop.

Analysis of cloud effects on long-term global and diffuse photosynthetically active radiation at a Mediterranean site

This study addresses the analysis of the cloud effects on photosynthetically active radiation (PAR, 400–700 nm) for global (PARGlobal) and its diffuse component (PARDiffuse). To this aim, a 11-year database of measured PARGlobal and modeled PARDifuse recorded in a Mediterranean site was analyzed, for both clear-sky and all-sky scenarios. PARGlobal mean values for the entire period were estimated in (200 ± 50) Wm−2 and (240 ± 50) Wm−2 for all- and clear-sky scenarios, respectively, while the values obtained for PARDiffuse were (59 ± 6) Wm−2 for all-skies and (51 ± 5) Wm−2 for clear-skies. PARGlobal monthly averages show the typical annual pattern driven by the annual course of solar position and PARDiffuse presents a similar but less marked pattern. The observed seasonal behavior was explained in terms of cloud cover, cloud frequency, liquid and ice content for all-sky scenarios. Higher variability during spring was detected due to the more complex cloud features in this season. Cloud Radiative Effect (CRE) showed negative values associated with a decrease in PARGlobal (−36 ± 14) Wm−2 and positive values corresponding to an increase in PARDiffuse (+7 ± 5) Wm−2. A clear seasonal pattern was found for CREGlobal and CREDiffuse with higher values in spring and autumn, and lower values in summer and winter. Additionally, the PAR-to-Total ratio and diffuse fraction (kPAR) values were evaluated under different sky conditions. Monthly mean values of the PAR-to-Total ratio showed steady values around 0.44 and any dependence on clearness index (kt) nor total cloud cover (TCC) was found. However, kPAR seemed to increase with TCC, taking averages values of 0.45 for all-sky and 0.28 for clear-sky scenarios. For all-sky conditions a clear seasonal pattern was observed with higher values in colder months. A secondary maximum value for kPAR was found in summer for clear-sky scenarios, revealing the important effect of the Saharan dust intrusions in the Mediterranean region. Finally, a well-defined logistic relationship was found between kPAR and kt, leading to estimate kPAR from total solar irradiance measurements.

Measurement of diffuse and plane of array irradiance by a combination of a pyranometer and an all-sky imager

Accurate, robust and cost-efficient measurements of diffuse horizontal irradiance (DHI) and global tilted irradiance (GTI) are of great interest for solar energy applications. However, the available measurement techniques exhibit at least one of these shortcomings: restriction of GTI measurement to a single plane, intensive maintenance, high acquisition cost or increased deviations, especially at new measurement sites. To avoid these shortcomings, we suggest a comparably inexpensive and robust setup of a thermopile pyranometer and an all-sky imager (ASI) for measurement of DHI and GTI. The pyranometer measures global horizontal irradiance (GHI) and our method consecutively estimates diffuse sky radiance, DHI, direct normal irradiance (DNI) and GTI, by merging information from the combined setup. The system is developed and validated at two sites in Spain and Germany. Measurement of GTI is benchmarked for seven planes over GTI derived by transposition based on DHI and DNI from a tracker setup with a pyrheliometer and shaded thermopile pyranometer. Our results indicate that the measurement system can be applied at both sites. The proposed method avoids time-consuming radiometric calibrations of the camera by the combination of both sensors and a self-calibration. The measurement system is promising in particular for measurement of GTI. For 10-min average GTI, our approach yields an rRMSD of 1.6...4.8% for planes with tilts in the range of 20∘...61∘. Thus, at both sites and for all planes, it outperforms the tracker-based transposition yielding 2.3...6.5%. DHI is measured significantly more accurately than reported in previous works using an ASI alone.

Estimating global irradiance on tilted surfaces using a cubic multi-directional pyranometer array

The multi-directional pyranometer array is a method for estimation of the global irradiance on a tilted plane and the direct irradiance based on the simultaneous measurement of global irradiance on a set of stationary surfaces with different inclinations and orientations. Experimental testing shows that estimates of global irradiance on a tilted plane are 50% more accurate than transposing measured global and diffuse irradiance. A simplified layout of five sensors yields comparable results.

Evaluation of On-Line MPPT Algorithms for PV-Based Battery Storage Systems

This paper presents a novel Simulink models with an evaluation study of more widely used On-Line Maximum Power Point tracking (MPPT) techniques for Photo-Voltaic based Battery Storage Systems (PV-BSS). To have a full comparative study in terms of the dynamic response, battery state of charge (SOC), and oscillations around the Maximum Power Point (MPP) of the PV-BSS to variations in climate conditions, these techniques are simulated in Matlab/Simulink. The introduced methodologies are classified into two types; the first type is conventional hill-climbing techniques which are based on instantaneous PV data measurements such as Perturb & Observe and Incremental Conductance techniques. The second type is a novel proposed methodology is based on using solar irradiance and cell temperature measurements with pre- build Adaptive Neuro-Fuzzy Inference System (ANFIS) model to predict DC–DC converter optimum duty cycle to track MPP. Then evaluation study is introduced for conventional and proposed On-Line MPPT techniques. This comparative study can be useful in specifying the appropriateness of the MPPT techniques for PV-BSS. Also the introduced model can be used as a valued reference model for future research related to Soft Computing (SC) MPPT techniques. A significant improvement of SOC is achieved by the proposed model and methodology with high accuracy and lower oscillations.

초정밀 가공을 이용한 다층 패턴 구조의 각도-독립적 구조색 구현

Structural color refers to a phenomenon, in which white light is influenced by the structure and the color is expressed. It can express various colors depending on the structures and has an advantage in terms of durability compared to the conventional dyeing processes. However, the structural color from a single layer easily varies depending on the light irradiation angle or measurement angle. Hence, numerous studies have been conducted to improve the angle-independency of structural color by fabricating multi-layers with complex nano-patterns. But, they usually require highly controlled processing environments and are not suitable for large-area manufacturing. Therefore, this study aims to develop a fabrication process by utilizing ultra-precision machining and silicon molding. Here, angle-independent structural colors were implemented with multi-layer micro-patterned films. Through the experiments, angle-independency was evaluated by varying the number of silicon films and measurement angles. RGB and HSV values were extracted from the images and applied for quantitative analysis. The suggested fabrication process successfully exhibited the angle-independent structural color with given patterns and film thickness. It is expected that this study can contribute to improving the multi-layer fabrication processes concerning machinability and large-area production.

High resolution measurement network of global horizontal and tilted solar irradiance in southern Germany with a new quality control scheme

Upscaling techniques are applied to estimate the total power feed-in by PV systems in control areas by grid operators. In order to improve the real-time upscaling of PV power production in the control area of the German transmission system operator TransnetBW we set up a network of 40 solar irradiance measurement stations in southern Germany. The measurement stations consist of a pyranometer, measuring global horizontal irradiance, and three silicon cells oriented east, south, and west with tilt angles of 25°, measuring global tilted irradiance. Irradiance values in minute resolution are transferred in near real-time. In order to ensure a good quality of our data we have developed a new quality control scheme exploiting the sensors with different orientations. Starting with data from September 2020, we will make the quality controlled minute-resolution measurements publicly available on a monthly basis.

Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data

Near-earth hyperspectral big data present both huge opportunities and challenges for spurring developments in agriculture and high-throughput plant phenotyping and breeding. In this article, we present data-driven approaches to address the calibration challenges for utilizing near-earth hyperspectral data for agriculture. A data-driven, fully automated calibration workflow that includes a suite of robust algorithms for radiometric calibration, bidirectional reflectance distribution function (BRDF) correction and reflectance normalization, soil and shadow masking, and image quality assessments was developed. An empirical method that utilizes predetermined models between camera photon counts (digital numbers) and downwelling irradiance measurements for each spectral band was established to perform radiometric calibration. A kernel-driven semiempirical BRDF correction method based on the Ross Thick-Li Sparse (RTLS) model was used to normalize the data for both changes in solar elevation and sensor view angle differences attributed to pixel location within the field of view. Following rigorous radiometric and BRDF corrections, novel rule-based methods were developed to conduct automatic soil removal; and a newly proposed approach was used for image quality assessment; additionally, shadow masking and plot-level feature extraction were carried out. Our results show that the automated calibration, processing, storage, and analysis pipeline developed in this work can effectively handle massive amounts of hyperspectral data and address the urgent challenges related to the production of sustainable bioenergy and food crops, targeting methods to accelerate plant breeding for improving yield and biomass traits.

PV generator nominal power estimation using a ground sensor and the PVLIB online irradiance database

The nominal power of a photovoltaic generator is a crucial parameter. For instance, it is a required input parameter for modelling the energy produced by a PV system. Determining the nominal power in-situ requires measuring the irradiance and module temperature. Usually, the irradiance is measured with a sensor placed on the ground and at the plane of the array. The present work first compares the irradiance measurements of a calibrated PV module with the openly available irradiance database PVLIB. Second, it contrasts the daily estimated nominal power values considering both irradiance sources. The results indicate that the irradiance database is suitable to calculate the nominal power.