Solar Irradiance Sensors Research Articles

Advanced simulation-based predictive modelling for solar irradiance sensor farms

ABSTRACT The need for accurate solar power forecasting is critical for grid stability as solar energy becomes more prevalent. This paper presents a new framework called Cloud-based Analysis and Integration for Data Efficiency (CAIDE) for real-time monitoring and forecasting of solar irradiance in sensor farms. CAIDE can handle multiple sensor farms, enhance predictive models in real-time, and is built on Model Based Systems Engineering (MBSE) and Internet of Things (IoT) technologies. It can correct its forecasts, ensuring they stay current, and operates on various architectures, ensuring scalability. Tested on multiple sensor farms, CAIDE proved to be scalable and improved the initial accuracy of solar power production forecasts in real-time. This framework is significant for solar plant deployment and the advancement of renewable energy.

Towards the definition of a solar forcing dataset for CMIP7

Abstract. The solar forcing prepared for Phase 6 of the Coupled Model Intercomparison Project (CMIP6) has been used extensively in climate model experiments and has been tested in various intercomparison studies. Recently, an International Space Science Institute (ISSI) working group has been established to revisit the solar forcing recommendations, based on the lessons learned from CMIP6, and to assess new datasets that have become available, in order to define a road map for building a revised and extended historical solar forcing dataset for the upcoming Phase 7 of CMIP. This paper identifies the possible improvements required and outlines a strategy to address them in the planned new solar forcing dataset. Proposed major changes include the adoption of the new Total and Spectral Solar Irradiance Sensor (TSIS-1) solar reference spectrum for solar spectral irradiance and an improved description of top-of-the-atmosphere energetic electron fluxes, as well as their reconstruction back to 1850 by means of geomagnetic proxy data. In addition, there is an urgent need to consider the proposed updates in the ozone forcing dataset in order to ensure a self-consistent solar forcing in coupled models without interactive chemistry. Regarding future solar forcing, we propose consideration of stochastic ensemble forcing scenarios, ideally in concert with other natural forcings, in order to allow for realistic projections of natural forcing uncertainties.

Advancements in solar spectral irradiance measurements by the TSIS-1 spectral irradiance monitor and its role for long-term data continuity

The first implementation of NASA’s Total and Spectral Solar Irradiance Sensor (TSIS-1) launched on December 15th, 2017, and was integrated into the International Space Station (ISS) to measure both the total solar irradiance (TSI) and the solar spectral irradiance (SSI). The direct measurement of the SSI is made by the LASP Spectral Irradiance Monitor (SIM) and provides data essential to interpreting how the Earth system responds to solar spectral variability. Extensive advances in TSIS-1 SIM instrument design and new SI-traceable spectral irradiance calibration techniques have resulted in improved absolute accuracy with uncertainties of less than 0.5% over the continuous 200–2400 nm spectral range. Furthermore, improvements in the long-term spectral stability corrections provide lower trend uncertainties in SSI variability measurements. Here we present the early results of the TSIS-1 SIM measurements covering the first 5 years of operations. This time period includes the descending phase of solar cycle 24, the last solar minimum, and the ascending phase of solar cycle 25. The TSIS-1 SIM SSI results are compared to previous measurements both in the absolute scale of the solar spectrum and the time dependence of the SSI variability. The TSIS-1 SIM SSI spectrum shows lower IR irradiance (up to 6% at 2400 nm) and small visible increases (~0.5%) from some previous reference solar spectra. Finally, initial comparisons are made to current NRLSSI2 and SATIRE-S SSI model results and offer opportunities to validate model details both for short-term (solar rotation) spectral variability and, for the first time, the longer-term (near half solar cycle) spectral variability across the solar spectrum from the UV to the IR.

Fuzzy-IoT smart irrigation system for precision scheduling and monitoring

Adopting and developing sustainable precision irrigation systems allows for improving productivity, reducing water and energy losses in agricultural fields.This paper deals with the development and real-time implementation of a smart precision irrigation system (SPIS). The proposed system combines two irrigation approaches, processed by a feedback fuzzy logic (FL) controller, and long-range data transmission and monitoring via the LoRa protocol. The combined approaches not only enhance overall performance but also address several limitations commonly associated with existing approaches. The proposed system uses the collected data from the sensors unit to provide the optimal irrigation decisionand communicates it with the operator (farmer) via a developed IoT platform. Data from soil moisture, ambient temperature, solar irradiance, and rainfall sensors are processed through the FL controller to adjust the irrigation times. The inputs and outputs are fuzzified by trapezoidal and triangular membership functions. Mamdani's fuzzy inference is used to control the system through a set of linguistic control rules for a more accurate decision for each scenario. The field experimentation of the developed SPIS in the eastern region of Morocco shows high efficiency and reliability by saving water and energy compared to traditional irrigation control. The proposed system, with remote data monitoring, promotes better management of irrigation, water, and energy consumption, and it is suitable to be mounted with any type of irrigation installation in the field, without extensive modifications.

Design and Construction of Ultraviolet and Incoming Solar Irradiance Sensing Device

In-situ measurements of ultraviolet (UV) and solar irradiance is very sparse in Nigeria because of cost; it is estimated using meteorological parameters. In this work, a low-cost UV and pyranometer device, using locally sourced materials, was developed. The instrument consists of a UV sensor (ML8511), a photodiode (BPW34) housed in a carefully sealed vacuumed glass bulb, the UV and solar irradiance sensor amplifiers, a 16-bit analog-to-digital converter (ADS1115), Arduino mega 2560, liquid crystal display (LCD) and microSD card for data logging. The designed amplifier has an offset voltage of 0.8676 mV. The sensitivity of the irradiance device is 86.819 Wm-2/mV with a correcting factor of 27.77 Wm-2 and a maximum range of 1200 Wm-2. The instrument validation error is 9.67% and a correlation coefficient of 0.89 when compared with a standard SRS100 pyranometer. The UV sensor showed a close response with a correlation of 0.99 in comparison with a standard Skye instrument. From 08:00 to 16:00 local time (LT), there is a very close agreement between the standard device and the developed counterpart, with marginal differences of about 9.6% observed at the two extremes.

Energy efficient route prediction for solar powered vehicles

Solar powered vehicles are currently being developed towards entirely self-sustaining vehicles that harness their energy directly from the sun. For such vehicles, it is important to optimise their solar exposure while driving, thereby reducing their energy consumption through fossil fuels. Research has emerged to estimate optimised routes for solar vehicles, and this paper builds on this work to expand on the parameters used to calculate the route, thereby improving the energy-harnessing quality of the route together with its overall utility for the driver. The ArcGIS tool and the open weather API are used to predict the solar potential of a vehicle by taking into account shade based on surrounding topography, vehicle type, weather, distance and time of day. The model was implemented as a user mobile application ‘Drive Solar’ that calculates the optimal route for the user based on their preferences for time and energy efficiency. The effectiveness of the prediction model was tested using a solar irradiance sensor in Dublin city. The results show that the model predicts the route with the most energy absorbed with a 51.65% accuracy and chooses the route with the most energy consumed with a 86.65% accuracy. We conclude that Drive Solar can aid in the transition to widespread use of self-sustaining solar vehicles.

Short-Term Solar Irradiance Prediction Based on Adaptive Extreme Learning Machine and Weather Data.

Concerns over fossil fuels and depletable energy sources have motivated renewable energy sources utilization, such as solar photovoltaic (PV) power. Utilities have started penetrating the existing primary grid with renewable energy sources. However, penetrating the grid with photovoltaic energy sources degrades the stability of the whole system because photovoltaic power depends on solar irradiance, which is highly intermittent. This paper proposes a prediction method for non-stationary solar irradiance. The proposed method uses an adaptive extreme learning machine. The extreme learning machine method uses approximated sigmoid and hyper-tangent functions to ensure faster computational time and more straightforward microcontroller implementation. The proposed method is analyzed using the hourly weather data from a specific site at Najran University. The data are preprocessed, trained, tested, and validated. Several evaluation metrics, such as the root mean square error, mean square error, and mean absolute error, are used to evaluate and compare the proposed method with other recently introduced approaches. The results show that the proposed method can be used to predict solar irradiance with high accuracy, as the mean square error is 0.1727. The proposed approach is implemented using a solar irradiance sensor made of a PV cell, a temperature sensor, and a low-cost microcontroller.

Performance Analysis of Solar Panels in Tropical Region: A Study Case in Surakarta Indonesia

The performance of solar panels affects the utilization of solar energy for daily life. This study aims to carry out the measurement of the performance of solar panels in Surakarta City is located between 110 ° 45 '15" and 110 ° 45" 35" East Longitude and between 7 ° 36" and 7 ° 56" South Latitude. Research used solar panels, current sensors, voltage sensors, temperature sensors, solar irradiance sensors, humidity sensors, Arduino and Labview. The solar panels 20 WP is used in the experiment. The measurement results obtained that the maximum energy value per day produced is 165 Wh and a minimum of 76.8 Wh with an average of 109.1 Wh. Temperature measurements were carried out in the range 37.2 0 C to 41.0 0C which is the normal temperature for PV operations. The average irradiation measurement is 1834.3 W/m2 while the average humidity is 32.5%. The relationship between energy and temperature, energy with solar irradiance and energy with humidity find using Pearson Product Moment Correlation (PPMC). The result show that the effect of temperature and solar irradiance were more significant than humidity.

A Novel PV Maximum Power Point Tracking Based on Solar Irradiance and Circuit Parameters Estimation

This research paper presents a novel maximum power point taking (MPPT) algorithm. The algorithm uses an adaptive calculation block to estimate the solar irradiance and the PV I–V curve circuit parameters based on the PV panel’s measured output current and voltage. In the proposed algorithm, the output power does not oscillate around the maximum power point (MPP) compared to conventional MPPT methods. Moreover, the proposed algorithm does not require expensive solar irradiance sensors compared with trackers that depend on measured solar irradiance. In addition, the proposed MPPT can handle the fast variation in solar irradiance. The PV panel nonlinear I–V curve was modeled using a single-diode PV. The algorithm with the adaptive block was tested separately to verify the ability of the system to estimate the solar irradiance and the circuit parameters. The solar system was then simulated using MATLAB/Simulink to evaluate the robustness of the proposed method under steady-state and during sudden changes in solar irradiance and load. The proposed solar system reaches the steady-state in 8 ms after a step-change in the solar irradiance. In the worst-case scenario, the proposed system achieves a relative error of around 2.64% in estimating the solar irradiance at 600 W/m2 with an efficiency of 99.3%.

Estimates of Hyperspectral Surface and Underwater UV Planar and Scalar Irradiances from OMI Measurements and Radiative Transfer Computations

Quantitative assessment of the UV effects on aquatic ecosystems requires an estimate of the in-water hyperspectral radiation field. Solar UV radiation in ocean waters is estimated on a global scale by combining extraterrestrial solar irradiance from the Total and Spectral Solar Irradiance Sensor (TSIS-1), satellite estimates of cloud/surface reflectivity, ozone from the Ozone Monitoring Instrument (OMI) and in-water chlorophyll concentration from the Moderate Resolution Imaging Spectroradiometer (MODIS) with radiative transfer computations in the ocean-atmosphere system. A comparison of the estimates of collocated OMI-derived surface irradiance with Marine Optical Buoy (MOBY) measurements shows a good agreement within 5% for different seasons. To estimate scalar irradiance at the ocean surface and in water, we propose scaling the planar irradiance, calculated from satellite observation, on the basis of Hydrolight computations. Hydrolight calculations show that the diffuse attenuation coefficients of scalar and planar irradiance with depth are quite close to each other. That is why the differences between the planar penetration and scalar penetration depths are small and do not exceed a couple of meters. A dominant factor defining the UV penetration depths is chlorophyll concentration. There are other constituents in water that absorb in addition to chlorophyll; the absorption from these constituents can be related to that of chlorophyll in Case I waters using an inherent optical properties (IOP) model. Other input parameters are less significant. The DNA damage penetration depths vary from a few meters in areas of productive waters to about 30–35 m in the clearest waters. A machine learning approach (an artificial neural network, NN) was developed based on the full physical algorithm for computational efficiency. The NN shows a very good performance in predicting the penetration depths (within 2%).

Model-Based Power Management for Smart Farming Wireless Sensor Networks

A model-based strategy for an efficient power supply control used in a wireless sensor network is presented. The strategy, based on Pulse-Skipping Modulation, regulates the current charging a battery, delivered by a photovoltaic source, resulting in an accurate current regulation and highly efficient power management. The strategy is implemented on a microcontroller unit and compensates for the microcontroller self-absorbed current. The modulation signal is generated through a full software interface, reducing the requirement for external components. Experimental validations, performed on a charger prototype by using a laboratory photovoltaic device simulator, proved that both regulation accuracy, regulation resolution and converter efficiency achieved are superior to the classic Pulse-Width Modulation. The approach results in a simple practical implementation, carries over the advantages of an up-to-date model for the photovoltaic device, and serves the auxiliary purpose of using the photovoltaic source as an instantaneous solar irradiance sensor.

INSPIRE-SAT 7, a Second CubeSat to Measure the Earth’s Energy Budget and to Probe the Ionosphere

INSPIRE-SAT 7 is a French 2-Unit CubeSat (11.5 × 11.5 × 22.7 cm) primarily designed for Earth and Sun observation. INSPIRE-SAT 7 is one of the missions of the International Satellite Program in Research and Education (INSPIRE). Twice the size of a 4 × 4 Rubik’s Cube and weighing about 3 kg, INSPIRE-SAT 7 will be deployed in Low Earth Orbit (LEO) in 2023 to join its sister satellite, UVSQ-SAT. INSPIRE-SAT 7 represents one of the in-orbit demonstrators needed to test how two Earth observation CubeSats in orbit can be utilized to set up a satellite constellation. This new scientific and technological pathfinder CubeSat mission (INSPIRE-SAT 7) uses a multitude of miniaturized sensors on all sides of the CubeSat to measure the Earth’s energy budget components at the top-of-the-atmosphere for climate change studies. INSPIRE-SAT 7 contains also a High-Frequency (HF) payload that will receive HF signals from a ground-based HF transmitter to probe the ionosphere for space weather studies. Finally, this CubeSat is equipped with several technological demonstrators (total solar irradiance sensors, UV sensors to measure solar spectral irradiance, a new Light Fidelity (Li-Fi) wireless communication system, a new versatile telecommunication system suitable for CubeSat). After introducing the objectives of the INSPIRE-SAT 7 mission, we present the satellite definition and the mission concept of operations. We also briefly show the observations made by the UVSQ-SAT CubeSat, and assess how two CubeSats in orbit could improve the information content of their Earth’s energy budget measurements. We conclude by reporting on the potential of future missions enabled by CubeSat constellations.

Investigation of Applicability of Impact Factors to Estimate Solar Irradiance: Comparative Analysis Using Machine Learning Algorithms

This study explores investigation of applicability of impact factors to estimate solar irradiance by four machine learning algorithms using climatic elements as comparative analysis: linear regression, support vector machines (SVM), a multi-layer neural network (MLNN), and a long short-term memory (LSTM) neural network. The methods show how actual climate factors impact on solar irradiation, and the possibility of estimating one year local solar irradiance using machine learning methodologies with four different algorithms. This study conducted readily accessible local weather data including temperature, wind velocity and direction, air pressure, the amount of total cloud cover, the amount of middle and low-layer cloud cover, and humidity. The results show that the artificial neural network (ANN) models provided more close information on solar irradiance than the conventional techniques (linear regression and SVM). Between the two ANN models, the LSTM model achieved better performance, improving accuracy by 31.7% compared to the MLNN model. Impact factor analysis also revealed that temperature and the amount of total cloud cover are the dominant factors affecting solar irradiance, and the amount of middle and low-layer cloud cover is also an important factor. The results from this work demonstrate that ANN models, especially ones based on LSTM, can provide accurate information of local solar irradiance using weather data without installing and maintaining on-site solar irradiance sensors.

Overview of the Solar Radiation and Climate Experiment (SORCE) Seventeen-Year Mission

The Solar Radiation and Climate Experiment (SORCE) was a NASA mission that operated from 2003 to 2020 to provide key climate-monitoring measurements of total solar irradiance (TSI) and solar spectral irradiance (SSI). Three important accomplishments of the SORCE mission are i) the continuation of the 42-year-long TSI climate data record, ii) the continuation of the ultraviolet SSI record, and iii) the initiation of the near-ultraviolet, visible, and near-infrared SSI records. All of the SORCE instruments functioned well over the 17-year mission, which far exceeded its five-year prime mission goal. The SORCE spacecraft, having mostly redundant subsystems, was also robust over the mission. The end of the SORCE mission was a planned passivation of the spacecraft following a successful two-year overlap with the NASA Total and Spectral Solar Irradiance Sensor (TSIS) mission, which continues the TSI and SSI climate records. There were a couple of instrument anomalies and a few spacecraft anomalies during SORCE’s long mission, but operational changes and updates to flight software enabled SORCE to remain productive to the end of its mission. The most challenging of the anomalies was the degradation of the battery capacity that began to impact operations in 2009 and was the cause for the largest SORCE data gap (August 2013 – February 2014). An overview of the SORCE mission is provided with a couple of science highlights and a discussion of flight anomalies that impacted the solar observations. Companion articles about the SORCE instruments and their final science data-processing algorithms provide additional details about the instrument measurements over the duration of the mission.

Direct Influence of Solar Spectral Irradiance on the High-Latitude Surface Climate

AbstractNot only total solar irradiance (TSI) but also spectral solar irradiance (SSI) matter for our climate. Different surfaces can have different reflectivity for the visible (VIS) and near-infrared (NIR). The recent NASA Total and Spectral Solar Irradiance Sensor (TSIS-1) mission has provided more accurate SSI observations than before. The TSI observed by TSIS-1 differs from the counterpart used by climate models by no more than 1 W m−2. However, the SSI difference in a given VIS (e.g., 0.44–0.63 μm) and NIR (e.g., 0.78–1.24 μm) band can be as large as 4 W m−2 with opposite signs. Using the NCAR CESM2, we study to what extent such different VIS and NIR SSI partitions can affect the simulated climate. Two sets of simulations with identical TSI are carried out, one with SSI partitioning as observed by the TSIS-1 mission and the other with what has been used in the current climate models. Due to different VIS-NIR spectral reflectance contrasts between icy (or snowy) surfaces and open water, the simulation with more SSI in the VIS has less solar absorption by the high-latitude surfaces, ending up with colder polar surface temperature and larger sea ice coverage. The difference is more prominent over the Antarctic than over the Arctic. Our results suggest that, even for the identical TSI, the surface albedo feedback can be triggered by different SSI partition between the VIS and NIR. The results underscore the importance of continuously monitoring SSI and the use of correct SSI in climate simulations.

Cloud advection model of solar irradiance smoothing by spatial aggregation

Solar generation facilities are inherently spatially distributed and therefore aggregate solar irradiance in both space and time, smoothing its variability. To represent the spatiotemporal aggregation process, most existing studies focus on the reduced correlation in solar irradiance throughout a plant's spatial distribution. In this paper, we derived a cloud advection model that is instead based upon lagging correlations between upwind/downwind portions of a distributed plant, induced by advection of a fixed cloud pattern over the plant. We use the model to calculate a plant transfer function that can be used to predict the smoothing of the time series. The model was validated using the distributed HOPE-Melpitz measurement dataset, which consisted of 50 solar irradiance sensors at 1 s temporal resolution over a 3 × 2 km2 bounding area. The initial validation showed that the advection-based model outperforms other models at predicting the smoothed irradiance time series during manually identified, advection dominated conditions. We also conducted validation on the model against additional advection dominated periods in the dataset that were identified algorithmically. The cloud advection model's performance compared well to models in literature, but degraded slightly as larger cross-wind plant distributions were investigated. The results in this paper highlight the need to incorporate advection effects on spatial aggregation during advection dominated conditions. Future development of spatiotemporal aggregation models is needed to unify advective models with existing correlation reduction models and to identify regimes where each dominate.

Monitoring of Photovoltaic System Performance Using Outdoor Suns-VOC

Summary In-field characterization of photovoltaics is crucial to understand performance and degradation mechanisms, subsequently improving overall reliability and lifespans. Current outdoor characterization is limited by logistical difficulties, variable weather, and requirements to measure during peak production hours. We capitalize on Suns-VOC, which is widely used for laboratory measurements of single solar cells and discuss the barriers in extending the technique to outdoor systems. We demonstrate the normalization of measurements using both backsheet temperature sensors and on-site weather stations. Despite weather variation, VOC, ideality factor, and pseudo fill factor all fall within 1% of the laboratory measurements. It is also demonstrated that monitoring the system VOC at 0.05 to 0.1 suns, during minimal power production, provides a figure of merit that can indicate early degradation of the system. Extensive simulations show that shading portions of a system has minimal effect on measurements, allowing the technique to be used in all weather conditions.

EVALUATION OF THE RADIOMETRIC ACCURACY OF IMAGES OBTAINED BY A SEQUOIA MULTISPECTRAL CAMERA

ABSTRACT Geometric and radiometric corrections of images obtained by remotely piloted aircraft systems (RPAS) are required for the measurement of the spectral reflectance and calculation of vegetation indices. This study aimed to determine which radiometric calibration procedures allow obtaining suborbital images with more accurate reflectance data and spectral indices based on data collected by a Sequoia multispectral camera coupled to a RPAS on black oat cultivations at the flowering stage located in Eldorado do Sul, RS, Brazil. The orthophoto mosaics were processed without and with radiometric corrections by reflectance calibration ground target and/or solar irradiance sensor of the camera, and the radiometric accuracy of these images was evaluated using spectral reflectance ground data collected by the Crop Circle canopy sensor. The results indicated that the use of a reflectance calibration ground target is recommended to increase the radiometric accuracy of photographs even if the Sequoia solar irradiance sensor is employed. The use of the light gray target in the radiometric calibration resulted in orthophotos with the mean square error of reflectance values lower than 0.01 for all the analyzed bands and spectral indices.

Design and Implementation of Fuzzy Compensation Scheme for Temperature and Solar Irradiance Wireless Sensor Network (WSN) on Solar Photovoltaic (PV) System.

Photovoltaic (PV) systems need measurements of incident solar irradiance and PV surface temperature for performance analysis and monitoring purposes. Ground-based network sensor measurement is preferred in many near real-time operations such as forecasting and photovoltaic (PV) performance evaluation on the ground. Hence, this study proposed a Fuzzy compensation scheme for temperature and solar irradiance wireless sensor network (WSN) measurement on stand-alone solar photovoltaic (PV) system to improve the sensor measurement. The WSN installation through an Internet of Things (IoT) platform for solar irradiance and PV surface temperature measurement was fabricated. The simulation for the solar irradiance Fuzzy Logic compensation (SIFLC) scheme and Temperature Fuzzy Logic compensation (TFLC) scheme was conducted using Matlab/Simulink. The simulation result identified that the scheme was used to compensate for the error temperature and solar irradiance sensor measurements over a variation temperature and solar irradiance range from 20 to 60 °C and from zero up to 2000 W/m2. The experimental results show that the Fuzzy Logic compensation scheme can reduce the sensor measurement error up to 17% and 20% for solar irradiance and PV temperature measurement.

Degradation Correction of TSIS SIM

An understanding of solar variability over a broad spectral range and broad range of timescales is needed by scientists studying Earth’s climate. The Total and Spectral Solar Irradiance Sensor (TSIS) Spectral Irradiance Monitor (SIM), is designed to measure solar spectral irradiance (SSI) with unprecedented accuracy from 200 nm to 2400 nm. SIM started daily observations in March 2018. To maintain its accuracy over the course of its anticipated 5-year mission and beyond, TSIS SIM needs to be corrected for optical degradation, common for solar viewing instruments. The differing long-term trends of various independent solar-irradiance records attest to the challenge at hand. The correction of TSIS SIM for optical degradation is based on piecewise linear fits that bring the three instrument channels into agreement. It is fundamentally different to the correction applied to the TSIS SIM predecessor on SORCE. The correction facilitates reproducibility, uncertainty estimation and is measurement-based. Corrected, integrated TSIS SIM SSI agrees with independent observations of total solar irradiance to within 45 ppm as well as various solar-irradiance models. TSIS SIM SSI is available at: http://lasp.colorado.edu/lisird/ .