Global Radiation Research Articles

Dopamine Transporter Availability in Early Parkinson's Disease is Dependent on Sunlight Exposure.

Preliminary studies suggested seasonality of dopaminergic functioning, but it is unknown whether dopamine transporter (DAT) expression in humans is also dependent on the seasons. We, therefore, investigated seasonal and sunlight-dependent effects on DAT availability in early Parkinson's disease (PD) patients and healthy controls. DAT single-photon emission computed tomography scans (n = 730) were gathered from the Parkinson's Progression Marker Initiative (PPMI) database. We used global horizontal irradiance (GHI) as proxy for sun exposure/month and assessed associations between striatal DAT availability and season (autumn/winter versus spring/summer), GHI and latitude of the PPMI site. In PD patients, DAT availability in the left caudate nucleus was higher in spring/summer (B [standard error (SE)] = 0.05 [0.02], P = 0.03) and positively associated with higher sun exposure (B [SE] = 0.59 [0.22] × 10-3 , P = 0.007). Latitude (in degrees north) of the PPMI site was negatively associated with DAT availability in both PD and healthy controls. Striatal DAT availability may be influenced by daylight exposure. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Ultra-short-term forecasting of global horizontal irradiance (GHI) integrating all-sky images and historical sequences

Accurate minute solar forecasts play an increasingly crucial role in achieving optimal intra-day power grid dispatch. However, continuous changes in cloud distribution and coverage pose a challenge to solar forecasting. This study presents a convolutional neural network-long short-term memory (CNN-LSTM) model to predict the future 10-min global horizontal irradiance (GHI) integrating all-sky image (ASI) and GHI sequences as input. The CNN is used to extract the sky features from ASI and a fully connected layer is used to extract historical GHI information. The resulting temporary information outputs are then merged and forwarded to the LSTM for forecasting the GHI values for the next 10 min. Compared to CNN solar radiation forecasting models, incorporating GHI into the forecasting process leads to an improvement of 18% in the accuracy of forecasting GHI values for the next 10 min. This improvement can be attributed to the inclusion of historical GHI sequences and regression via LSTM. The historical GHI contains valuable meteorological information such as aerosol optical thickness. In addition, the sensitivity analysis shows that the 1-lagged input length of the GHI and ASI sequence yields the most accurate forecasts. The advantages of CNN-LSTM facilitate power system stability and economic operation. Codes of the CNN-LSTM model in the public domain are available online on the GitHub repository https://github.com/zoey0919/CNN-LSTM-for-GHI-forecasting.

Dietary niche partitioning of three Sky Island Sceloporus lizards as revealed through DNA metabarcoding.

Lizard diets are highly diverse and have contributed to the diversification, biogeographical distributions, and evolution of novel traits across this global radiation. Many parts of a lizard's ecology-including habitat preferences, foraging modes, predation risks, interspecific competition, and thermal constraints, among others-interact to shape diets, and dietary niche partitioning simultaneously contributes to co-occurrence within communities. We used DNA metabarcoding of fecal samples to identify prey items in the diets of three sympatric Sceloporus lizards in the Madrean Sky Islands of Arizona, USA. We found evidence for dietary niche partitioning between interacting species concomitant with their respective ecologies. We also compared diet composition between populations to understand how conserved or plastic species' diets are between different environments. Our findings suggest that habitat generalists are also diet generalists in this system, while the same may be true for specialists. The identification of prey items to much lower taxonomic levels than previously documented further reveals hidden diversity in the diets of these species and underscores the utility of metabarcoding for understanding the full complexity of lizard diets.

Explainable forecasting of global horizontal irradiance over multiple time steps using temporal fusion transformer

Accurate prediction of solar irradiance is essential for the successful integration of solar power plants into electrical systems. Despite recent advancements in deep learning technology yielding impressive results in solar forecasting, their lack of interpretability has hindered their widespread adoption. In this paper, we propose a novel approach that integrates a Temporal Fusion Transformer (TFT) with a McClear model to achieve accurate and interpretable forecasting performance. The TFT is a deep learning model that provides transparency in its predictions through the use of interpretable self-attention layers for long-term dependencies, recurrent layers for local processing, specialized components for feature selection, and gating layers to suppress extraneous components. The model is capable of learning temporal associations between continuous time-series variables, namely, historical global horizontal irradiance (GHI) and clear sky GHI, accounting for cloud cover variability and clear sky conditions that are often ignored by most machine learning solar forecasters. Additionally, it minimizes a quantile loss during training to produce accurate probabilistic forecasts. In this study, we evaluate the performance of hourly GHI forecasts on eight diverse datasets with varying climates: temperate, cold, arid, and equatorial, for multiple temporal horizons of 2, 3, 6, 12, and 24 h. The model is benchmarked against both climatological persistence for deterministic forecasting and Complete History Persistence Ensemble for probabilistic forecasting. To prove that our model is not location locked, it has been blind tested on four completely different datasets. The results demonstrate that the proposed model outperforms its counterparts across all forecast horizons.

Performance evaluation and comparative study of three 52-kW PV plants in India: a case study

Developing countries like India are rapidly transitioning from traditional energy sources to sustainable energy sources, due to the increase in demand and the depletion of fossil fuels. Grid-connected photovoltaic (PV) systems attract many investors, organizations, and institutions for deployment. This article studies and compares the performance evaluations of three 52-kW PV plants installed at an educational institution, SRMIST (SRM Institute of Science and Technology), in Tamil Nadu, India. This site receives an annual average temperature of 28.5°C and an average global horizontal irradiation of 160 kWh/m2/m. The prediction model for the 52-kW power plant is obtained using solar radiation, temperature, and wind speed. Linear regression model-based prediction equations are derived using the Minitab 16.2.1 software, and the results are compared with the real-time AC energy yield acquired from the three 52-kW plants for the year 2020. Furthermore, this 52-kW plant is designed using PVsyst V7.1.8 version software. The simulation results are compared with the energy yield from the plants in 2020 to identify the shortfall in the plant performance. The loss analysis for the plant is performed by obtaining the loss diagram from the PVsyst software. This study also proposes a methodology to study the commissioned PV plant's performance and determine the interaction between variables such as direct and diffused solar radiations, air temperature, and wind speed for forecasting hourly produced power. This article will motivate researchers to analyze installed power plants using modern technical tools.

Potential quantification and impact factors analysis of energy flexibility in residential buildings with preheating control strategies

Demand response (DR) is an effective method for achieving energy flexibility. Preheating can store a certain amount of energy in the building while simultaneously reducing the heat load. The energy flexibility of a building during the heating season mainly depends on the types of DR events, the thermal performance of the building envelope, and weather conditions. Analyzing the impact of these factors on the preheating performance and exploring their interactions can reveal the potential for energy flexibility. This study proposes a simulation-based method to evaluate the energy flexibility of a residential building in Kitakyushu, Japan from the perspectives of energy efficiency, load and energy reduction. Rule-based control strategies are applied to heating, ventilation, and air conditioning systems. The results show that compared to preheating temperature of 24 °C, high insulation building at 25 °C achieves energy efficiency improvement of 3.5%–14.8% and load reduction increase of 1.6%–22.2%. Compared to low insulation building, preheating at 25 °C for 1 h increases energy efficiency in high insulation building by 6.2%–10.3%, with corresponding load reduction increase of 1.0%–19.1%. Extending the preheating duration improves load reduction but leads to heat loss. Preheating during heating system startup should be avoided as it produces the worst energy flexibility. Moreover, statistical analysis identifies key indicators for energy reduction: the inside surface temperature of the exterior wall (Pearson correlation coefficient = 0.820, standard regression coefficient = 0.924), followed by global horizontal radiation and building insulation performance.

Sap flow modelling based on global radiation and canopy parameters derived from a digital surface model

Sap flow modelling based on global radiation and canopy parameters derived from a digital surface model

Photoluminescence imaging of field-installed photovoltaic modules in diffuse irradiance

Photoluminescence imaging under sunlight excitation has in the recent years been proposed as a promising inspection technique of field-installed photovoltaic modules. Virtually, all studies have been conducted in full sunlight and clear sky conditions. A study in which photoluminescence images had been acquired at an irradiance level below 100 W m−2 using the lock-in technique has shown the potential of these images with respect to defect analysis. Additionally, as on-site measurement techniques are subject to weather conditions, it would be favorable to extend the applicability of photoluminescence imaging to a wider range of irradiance levels. A method for photoluminescence imaging which does not use the lock-in technique for the purpose of filtering of sunlight was proposed recently. The present study extends the application of this approach to diffuse irradiance conditions. We demonstrate that the method gives valuable information about the modules also in the case of image acquisition under diffuse global in-plane irradiance as low as 40 W m−2. Photoluminescence images acquired under low irradiance are comparable to electroluminescence images acquired at 10% of short-circuit current. Photoluminescence imaging has also been conducted successfully from the rear of bifacial modules in these illumination conditions.

Diurnal Evolution and Estimates of Hourly Diffuse Radiation Based on Horizontal Global Radiation, in Cerrado-Amazon Transition, Brazil

In the Cerrado-Amazonian ecotone in the State of Mato Grosso, intensely altered by anthropic action, the knowledge and processes of energy conversion and energy balance are still incipient, making the monitoring and modeling of diffuse radiation essential for several applications. The objective of this study was to evaluate the seasonality of the diurnal evolution and estimate the hourly diffuse radiation (Hdh) and incident radiation in the horizontal plane between June 2011 and October 2016. The instantaneous measurements (5 min) of diffuse radiation underwent geometric, astronomical, and anisotropic corrections, with subsequent hourly integrations. The seasonality of diffuse radiation and its radiometric fractions was evaluated. The estimates were made considering total and seasonal data groupings (water stations in the region) and in different cloudiness classes (atmospheric transmissivity index—KTh). The diurnal behavior of diffuse radiation (Hdh) was similar to that of global radiation and at the top of the atmosphere, with maximum values at solar noon. The correlations between Kdh and KTh showed third-order polynomial behavior, with maximum observed values of Kdh ranging from 0.8 to 0.9, for KTh less than 0.2. Estimation equations based on radiometric fractions underestimated the values of diffuse radiation, with a better performance presented by models adjusted in annual data groupings. Among the parameterized models for estimating diffuse radiation obtained in the literature, those calibrated regionally in this study, together with those developed for tropical regions, presented better statistical performances.

A new accurate low-cost instrument for fast synchronized spatial measurements of light spectra

Abstract. We developed a cost-effective Fast-Response Optical Spectroscopy Time-synchronized instrument (FROST). FROST can measure 18 light spectra in 18 wavebands ranging from 400 to 950 nm with a 20 nm full-width half-maximum bandwidth. The FROST 10 Hz measurement frequency is time-synchronized by a global navigation satellite system (GNSS) timing pulse, and therefore multiple instruments can be deployed to measure spatial variation in solar radiation in perfect synchronization. We show that FROST is capable of measuring global horizontal irradiance (GHI) despite its limited spectral range. It is very capable of measuring photosynthetic active radiation (PAR) because 11 of its 18 wavebands are situated within the 400-to-700 nm range. A digital filter can be applied to these 11 wavebands to derive the photosynthetic photon flux density (PPFD) and retain information on the spectral composition of PAR. The 940 nm waveband can be used to derive information about atmospheric moisture. We showed that the silicon sensor has undetectable zero offsets for solar irradiance settings and that the temperature dependency as tested in an oven between 15 and 46 ∘C appears very low (−250 ppm K−1). For solar irradiance applications, the main uncertainty is caused by our polytetrafluoroethylene (PTFE) diffuser (Teflon), a common type of diffuser material for cosine-corrected spectral measurements. The oven experiments showed a significant jump in PTFE transmission of 2 % when increasing its temperature beyond 21 ∘C. The FROST total cost (< EUR 200) is much lower than that of current field spectroradiometers, PAR sensors, or pyranometers, and includes a mounting tripod, solar power supply, data logger and GNSS, and waterproof housing. FROST is a fully standalone measurement solution. It can be deployed anywhere with its own power supply and can be installed in vertical in-canopy profiles as well. This low cost makes it feasible to study spatial variation in solar irradiance using large-grid high-density sensor setups or to use FROST to replace existing PAR sensors for detailed spectral information.

Targeted Balloon Observations of Stratosphere‐To‐Troposphere Transport From a Mesoscale Convective System

AbstractStratosphere‐troposphere exchange (STE) is important to the global radiation budget because it is capable of inducing significant changes in trace greenhouse gases such as ozone and water vapor. In the extratropics, stratosphere‐to‐troposphere—or downward—transport (STT) dominates and is routinely driven by upper‐troposphere jet circulations and cyclones. Tropopause‐overshooting convection provides a more rapid STE pathway, but STT in convection is less understood and targeted observations are limited to a handful of fortuitous aircraft encounters. In this study, we seek to improve understanding of STT near thunderstorm anvils using balloon observations of ozone, ground‐based radar observations, visible satellite imagery, and model output. Radar and satellite imagery are used to identify and ultimately target convection driving the “ozone‐wrapping” process. Herein, observations from a single mesoscale convective system in central Oklahoma are reported. A significant ozone enhancement 40%–60% greater than background concentrations in the free troposphere was observed during the balloon ascent underneath and within the anvil of the storm. Backwards particle trajectories were calculated to rule out other sources of this feature such as long‐range transport of stratospheric air and smoke pollution from nearby wildfires. To our knowledge, this work presents the first targeted ozonesonde launch and sampling of this STT process.

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.

Prediction of Solar Irradiation in Africa using Linear-Nonlinear Hybrid Models

Solar irradiation prediction including Global Horizontal Irradiation (GHI) and Direct Normal Irradiation (DNI) is a useful technique for assessing the solar energy potential at specific locations. This study used five Artificial Neural Network (ANN) models and Multiple Linear Regression (MLR) to predict GHI and DNI in Africa. Additionally, a hybrid model combining MLR and ANNs was proposed to predict both GHI and DNI and improve the accuracy of individual ANN models. Solar radiation (GHI and DNI) and global meteorological data from 85 cities with different climatic conditions over Africa during 2001-2020 were used to train and test the models developed. The Pearson correlation coefficient was used to identify the most influential input variables to predict GHI and DNI. Two scenarios were proposed to achieve the goal, each with different input variables. The first scenario used influential input parameters, while the second incorporated geographical coordinates to assess their impact on solar radiation prediction accuracy. The results revealed that the suggested linear-nonlinear hybrid models outperformed all other models in terms of prediction accuracy. Moreover, the investigation revealed that geographical coordinates have a minimal impact on the prediction of solar radiation.

Spatiotemporal trend analysis of climate indices for the European continent

Abstract The objective of this study is to analyze and visualize the spatial distribution of trends for 74 climate indices on a monthly time-scale in direction, magnitude, and significance level at a resolution of 0.1° during the period of 1950–2021 over the European region. The Mann–Kendall and Sen's slope estimators reveal that growing degree days with mean air temperature >4 °C (gd4) and heating degree days with mean air temperature <17 °C (hd17) show the largest increase (0.93 °C August) and decrease (1.03 °C July), respectively. The universal thermal climate index (utci), relative humidity (rh), wind chill index (wci), global radiation (bio20), and potential evapotranspiration (pet) are of significant importance due to higher correlation and magnitude of change. Country-specific zoning shows the highest warmer days during August experienced by Bosnia and Herzegovina (southeastern Europe) and lower colder days during January by Belarus (eastern Europe). High wind and high utci were experienced by Liechtenstein (southeastern Europe) region during July. The highest wci was experienced by San Marino (southern Europe) in June and Portugal (southern Europe) in March. Bio20 and rh decline were experienced by Russia (eastern Europe) and Moldova (southeastern Europe) in May and September, respectively. Results are useful to mitigate the risk associated with each of the climate indices for specific European regions.

Smart Approaches for Evaluating Photosynthetically Active Radiation at Various Stations Based on MSG Prime Satellite Imagery

Photosynthetically active radiation (PAR) is the 400–700 nm portion of the solar radiation spectrum that photoautotrophic organisms including plants, algae, and cyanobacteria use for photosynthesis. PAR is a key variable in global ecosystem and Earth system modeling, playing a prominent role in carbon and water cycling. Alongside air temperature, water availability, and atmospheric CO2 concentration, PAR controls photosynthesis and consequently biomass productivity in general. The management of agricultural and horticultural crops, forests, grasslands, and even grasses at sports venues is a non-exhaustive list of applications for which an accurate knowledge of the PAR resource is desirable. Modern agrivoltaic systems also require a good knowledge of PAR in conjunction with the variables needed to monitor the co-located photovoltaic system. In situ quality-controlled PAR sensors provide high-quality information for specific locations. However, due to associated installation and maintenance costs, such high-quality data are relatively scarce and generally extend over a restricted and sometimes non-continuous period. Numerous studies have already demonstrated the potential offered by surface radiation estimates based on satellite information as reliable alternatives to in situ measurements. The accuracy of these estimations is site-dependent and is related, for example, to the local climate, landscape, and viewing angle of the satellite. To assess the accuracy of PAR satellite models, we inter-compared 11 methods for estimating 30 min surface PAR based on satellite-derived estimations at 33 ground-based station locations over several climate regions in Europe, Africa, and South America. Averaged across stations, the results showed average relative biases (relative to the measurement mean) across methods of 1 to 20%, an average relative standard deviation of 25 to 30%, an average relative root mean square error of 25% to 35% and a correlation coefficient always above 0.95 for all methods. Improved performance was seen for all methods at relatively cloud-free sites, and quality degraded towards the edge of the Meteosat Second Generation viewing area. A good compromise between computational time, memory allocation, and performance was achieved for most locations using the Jacovides coefficient applied to the global horizontal irradiance from HelioClim-3 or the CAMS Radiation Service. In conclusion, satellite estimations can provide a reliable alternative estimation of ground-based PAR for most applications.

Long-term implementation of a model predictive controller for a hydronic floor heating and cooling system in a highly glazed house in Canada

This article presents the results of a 182 day implementation of a model predictive controller (MPC) in a test house in Ottawa, Canada. The MPC controls the timing of hydronic floor heating and cooling within the highly glazed house (13.5% south-facing window-to-floor ratio). The objectives of the MPC are to optimally control the on/off times of active heating and cooling to minimize energy use while also minimizing air temperature violations outside of a prescribed range. It was found that 71% of the days in the experimental period saw successful operation of the MPC towards these goals. Days that experienced overheating were found to be concentrated in January–February and days that experienced overcooling were found to be concentrated in March–May. Problematic decision making by the MPC was found to be rooted in 4 main issues: (1) global horizontal irradiance (GHI) is a bad indicator of passive solar on its own; (2) weather forecasting (specifically GHI forecasting) is critical to MPC success in highly glazed buildings; (3) the GHI history regressor used in the prediction model regularly influenced predictions illogically; and (4) the MPC lacked decision-making logic on the timing of hydronic floor cooling.

Design and Development of Grid-Connected Solar PV Power Plant Using PVsyst

As we know, the biggest problem that threatens today's world is climate change and global warming. Alongside that, Afghanistan does not have a reliable source of power, and people who live in cities do not have full access to electricity. So, we need immediate access to electricity. Therefore, among all other renewable sources, the only one that is feasible in the target area and could solve this problem soon is investing in a solar PV power plant. This study aims to develop a standard procedure for the design of grid-connected solar PV systems using PVsyst software. The project began with a broad database of meteorological data, including global daily horizontal solar irradiance, and also a database of various renewable energy system components from different manufacturers. This paper will explain the grid solar power limited in the year 2023. The photovoltaic power plant has a solar radiation of 6.22 KWh/Sq./day, covering 162.66 acres of land. The operating module temperature varies from -40°C to 85°C, with a tilt angle of 32 degrees. The various power losses (PV losses due to irradiation level, temperature, soiling, inverter, wiring, power electronics, grid availability, and interconnection) amount to 2006 KWh/m2, with a total production of 60513314 KVAh/year, and the performance ratio is calculated from simulation, giving an annual PR of 75.16%.

On-Grid Mono-Si PV Solar System Performance Evaluation in Baghdad

Abstract The performance assessment of a 5 kWp On-Grid Mon-crystalline silicon photovoltaic( PV) solar system is the subject of the present paper. This PV system is located in Baghdad city, Al-Mansour company. The present PV solar system is observed during 2018. The current system’s yearly daily averages for the array yield final yield, and the reference yield are 4.65 kWh/kWp, 4.5 kWh/kWp, and 5.9 kWh/kWp, respectively. The final yield’s yearly energy is 1613.8 kWh/kWp. The present year’s electrical energy output from this system is 7891.57 kWh, and Baghdad’s annual global horizontal solar irradiation (GHI) is 1987.5 kWh/m2. The average yearly daily losses for the system and the array are 0.15 kWh/kWp and 1.33 kWh/kWp, respectively. The average yearly efficiencies for the system, the array and the inverter are 11.33%, 11.7%, and 96.75%, respectively. The annual daily averages for performance ratio and capacity factor are 74.7% and 17.75 %, respectively. Mono-crystalline silicon (Mono-Si) seems to operate well in the environment of Baghdad-Iraq.

Optimum yearly and seasonal tilt angle of solar system in the center of Babylon/Iraq using PVsyst software

The photovoltaic system tilt angle is one of the more significant factors for obtaining the maximum solar energy that will fall on the PV panel. Consequently, then obtain maximum power output, the solar array needs to be angled properly. The analysis for a stand-alone system is simulated and modelled using PVsyst software version-7.2 for system power 3,120 Wp to obtain the optimum value of the tilt angles of maximum solar irradiation in the center of Babylon Governorate, Iraq. The ideal tilt angle for the south has been found for both yearly and the seasons. Different tilt angles were taken which were (29°, 30°, and 31°) annually, azimuth angle for all is (0°). The incident global irradiation in the collector plane, Incident beam irradiation in the collector plane is maximum at tilt angle (29°) that produces maximum available energy 5,132 kWh/kWp/year. same processing for annual different tilt angle was taken for seasonally; the results show that the angle (49°) for summer and (13°) for winter are the optimum tilt angle which obtains the maximum incident global irradiation in the collector plane, maximum incident beam irradiation in the collector plane at this angle then product maximum available energy 5,343 kWh/kWp/year.

Monitoring intertidal golden tides dominated by Ectocarpus siliculosus using Sentinel-2 imagery

Golden seaweed tides are a global environmental and social problem, that have been occurring along Ireland's eastern coastline since the 1990s. This study focused on analysing the spatiotemporal dynamics of golden seaweed (Ectocarpus siliculosus) coverage at Dollymount Strand in Dublin Bay (Ireland), between 2016 and 2022, and its relationship with meteorological conditions. Hyperspectral measurements in the field and Sentinel-2 imagery were utilized to monitor macroalgal blooms with a spatiotemporal resolution of 10 m and minimum costs. The normalized difference vegetation index (NDVI) values filtered between 0.1 and 1 were used to identify the coverage of golden seaweeds. The results showed that the golden seaweed coverage extended over 99% of the study area (54 ha) in June 2020 with an average NDVI of 0.25. A seasonal pattern of golden seaweed abundance was observed and modelled from May to October using a Generalized Additive Model. Approximately 28% of the coverage was correlated with daily average global radiance, and 38% of the mean NDVI was associated with daily average maximum air temperatures and wind direction by means of the Generalized Linear Models. A greater biomass of Ectocarpus spp. was accumulated on the beach when the wind direction was from the north-east and south-east. The results also suggested that freshwater nutrient inputs in winter from nearby estuaries may be contributing to golden tides on the shoreline. These findings are useful in developing strategies aimed at controlling and managing golden tides globally, as well as using them as bioindicators of ecological status in coastal environments.