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In the realm of sustainable energy, photovoltaic (PV) technology stands as a pivotal solution for clean energy generation. Continual advancements in PV technology have ushered in more efficient and cost-effective components, yet the precise localization and tracking of the maximum power point (MPP) remain critical for enhancing PV system performance. Over the years, various Maximum Power Point Tracking (MPPT) algorithms like Perturb and Observe (P&O) and Incremental Conductance (INC) have been established to address this challenge. In this proposed study, a novel approach using Gaussian Process Regression (GPR) for MPPT in PV systems is introduced. The GPR model is designed to predict the MPP accurately, thereby enhancing system efficiency and contributing to the advancement of sustainable energy generation. Implemented within the MATLAB/Simulink environment, the model utilizes a dataset comprising 1000 observations of solar irradiance, temperature, and corresponding voltages, where 120 datapoints among these were taken as sample for training and evaluation. The results demonstrate promising outcomes with a Mean Squared Error (MSE) of 1.2783 x 10-5 and a Root Mean Squared Error (RMSE) of 0.0031, indicating high accuracy in predicting the MPP. This study underscores the effectiveness of GPR in optimizing PV system performance, supporting its adoption for sustainable energy applications and paving the way for further advancements in renewable energy technologies. [ABSTRACT FROM AUTHOR]

Solar energy (SE) is vital for renewable energy generation, but its natural fluctuations present difficulties in maintaining grid stability and planning. Accurate forecasting of solar irradiance (SI) is essential to address these challenges. The current research presents an innovative forecasting approach named as Transformer-Infused Recurrent Neural Network (TIR) model. This model integrates a Bi-Directional Long Short-Term Memory (BiLSTM) network for encoding and a Gated Recurrent Unit (GRU) network for decoding, incorporating attention mechanisms and positional encoding. This model is proposed to enhance SI forecasting accuracy by effectively utilizing meteorological weather data, handling overfitting, and managing data outliers and data complexity. To evaluate the model's performance, a comprehensive comparative analysis is conducted, involving five algorithms: Artificial Neural Network (ANN), BiLSTM, GRU, hybrid BiLSTM-GRU, and Transformer models. The findings indicate that employing the TIR model leads to superior accuracy in the analyzed area, achieving R2 value of 0.9983, RMSE of 0.0140, and MAE of 0.0092. This performance surpasses those of the alternative models studied. The integration of BiLSTM and GRU algorithms with the attention mechanism and positional encoding has been optimized to enhance the forecasting of SI. This approach mitigates computational dependencies and minimizes the error terms within the model. [ABSTRACT FROM AUTHOR]

Renewable energy sources are increasing globally, mainly due to efforts to achieve net zero emissions. In Brazil, solar photovoltaic electricity generation has grown substantially in recent years, with the installed capacity rising from 2455 MW in 2018 to 47,033 MW in August 2024. However, the intermittency of solar energy increases the challenges of forecasting solar generation, making it more difficult for decision-makers to plan flexible and efficient distribution systems. In addition, to forecast power generation to support grid expansion, it is essential to have adequate data sources, but measured climate data in Brazil is limited and does not cover the entire country. To address this problem, this study evaluates the global horizontal irradiance (GHI) of four global reanalysis datasets—MERRA-2, ERA5, ERA5-Land, and CFSv2—at 35 locations across Brazil. The GHI time series from reanalysis was compared with ground-based measurements to assess its ability to represent hourly GHI in Brazil. Results indicate that MERRA-2 performed best in 90% of the locations studied, considering the root mean squared error. These findings will help advance solar forecasting by offering an alternative in regions with limited observational time series measurements through the use of reanalysis datasets. [ABSTRACT FROM AUTHOR]

The rapid global transition toward renewable energy necessitates innovative solar PV deployment strategies beyond conventional roof installations. In this context, commercial building facades represent an expansive yet underutilized resource for solar energy harvesting in urban areas. However, existing studies on commercial rooftop solar PV predominantly focus on European contexts, neglecting the unique design constraints and performance trade-offs present in regions such as the Middle East. This study addresses this gap by specifically investigating the impact of architectural and structural features on the utilizable facade area for PV deployment in commercial buildings within the hot desert climate of Saudi Arabia. Detailed case studies of twelve representative buildings are conducted, combining architectural drawing analysis, on-site measurements, and stakeholder surveys. The methodology identified sixteen parameters across three categories—facade functionality, orientation suitability, and surrounding obstructions—that impose technical and non-technical restrictions on photovoltaic integration 3D modeling, and irradiance simulations revealed that, on average, just 31% of the total vertical facade area remained suitable for PV systems after accounting for the diverse architectural and contextual limitations. The study considered 698 kWh/m2 of solar irradiance as the minimum threshold for PV integration. Shopping malls displayed the lowest utilizability, with near-zero potential, as extensive opaque construction, brand signage, and shading diminish viability. Offices exhibited the highest utilizability of 36%, owing to glazed facades and unobstructed surroundings. Hotels and hospitals presented intermediate potential. Overall, the average facade utilizability factor across buildings was a mere 16%, highlighting the significant hurdles imposed by contemporary envelope configurations. Orientation unsuitability further eliminated 12% of the initially viable area. Surrounding shading contributed an additional 0.92% loss. The results quantify the sensitivity of facades to aspects such as material choices, geometric complexity, building form, and urban context. While posing challenges, the building facade resource holds immense untapped potential for solar-based urban renewal. The study highlights the need for early architectural integration, facade-specific PV product development, and urban planning interventions to maximize the renewable energy potential of commercial facades as our cities rapidly evolve into smart solar energy landscapes. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) energy is one of the most prominent topics that have attracted the attention of researchers in recent years. The use of solar energy is increasing rapidly in the world. Although using PV energy has various advantages, it has some disadvantages. Among these disadvantages, power factor (PF) and total harmonic distortion (THD) issues are discussed in this article. When solar PV systems are integrated into the grid, various power quality problems arise. In addition, due to low power quality and high harmonics, power system components overheat and start operating in undesirable regions; causes great damage. The magnitude of PF and THD is dependent on solar irradiation values. In order to determine how the power quality in the grid-connected solar system is affected by changes in solar irradiation (G), results for various irradiation situations are presented and analyzed. In addition, at low irradiance values, the amplitude of harmonic components and reactive power increases, whereas the power factor of the PV system decreases. Low power factor and high amplitude of harmonics cause the efficiency of the solar system to decrease. In this study, PF and THDI values were measured on a particular cloudy day for analysis. An analysis of the solar PV system was conducted using Matlab/simulation program to model the grid-connected PV system. Thus, the analytical expression of the PF and THDI, which are dependent on irradiation, was found with a new method by using the Statistical Package for the Social Sciences (SPSS) program and the curve fitting method. Obtaining the analytical expressions for both solar irradiation (G) and power factor (PF) used the SPSS program and also solar irradiation (G) and total harmonic distortion (THDI) used the MATLAB curve fitting method which contributed to the science comparing to the existing literature. It can be prevented the low power quality by using such these expressions at low solar irradiation cases. [ABSTRACT FROM AUTHOR]

In recent years, solar irradiance forecasting has become essential to managing, developing, and effectively integrating photovoltaic (PV) systems properly into the smart grid. The foundation of a conventional variational autoencoder (VAE) is an entirely coupled layer that includes both decoder and encoder components. In this study, a novel deep attention-driven model for forecasting named bidirectional long short-term memory (BiLSTM) which is combined with the VAE model is introduced as an enhanced version of the VAE. BiLSTM is integrated at the encoder side of VAE to effectively extract and learn temporal dependencies that are embedded in the panel irradiance data. Additionally, a self-attention mechanism (SAM) is added to bilateral variational autoencoder (BiVAE) which is known as BiVAE-SAM that highlights the important characteristics. The proposed BiVAE-SAM permits the VAE's capacity to design the temporal dependency. The examined models are assessed using sun irradiance measurements from New York City, Turkey, Canopy, Los Angeles, California, and Florida. The outcomes exhibit that the proposed BiVAESAM model performs better mean absolute percentage error (MAPE) with values of 1.7935, 0.7828, 1.3491 and 2.8346 respectively for California, Los Angeles, New York City, and Florida, over existing stacked denoising autoencoders (SDA) model at projecting solar irradiance. [ABSTRACT FROM AUTHOR]

In the field of renewable energies, the logistical intricacies of production, as well as the use and storage of photovoltaic energy, have become critical issues. In addition to sheer quantity, the stability of this type of energy is a crucial factor in ensuring the reliability and consistency of power generation. This paper defines Solar Irradiance Stability Factors (SISFs) as indicators complementing electricity production. When planning solar energy production in each geographical area, both the quantity and stability of solar irradiance are necessary for exploitation and determining the quality of solar irradiance. While the average production of solar energy per unit area in each time interval is a widely used parameter in daily practice, the observation of the amplitude of solar irradiance and its influence on energy production in the observed time interval is currently still rare. The SISFs defined in this article are new metrics that mainly depend on the meteorological variability in an area, and the observed time intervals should be in the range of seconds, minutes, or even hours. Larger time intervals are not helpful for the stability of solar irradiance in energy production and logistics from the source to the destination. They provide a complementary and more accurate measure of how suitable a particular geographical area is for producing solar energy. [ABSTRACT FROM AUTHOR]

The electrification of vehicles has emerged as a pivotal technique for addressing environmental concerns and reducing reliance on conventional fuel sources. Conversely, the best way to incorporate renewable energy into electric vehicles (EVs) is still a challenging task, particularly in enhancing the performance of EVs through efficient energy management. The transition to EVs has gained momentum as part of global efforts to mitigate environmental impacts and reduce dependence on fossil fuels. This paper proposes a hybrid method for enhancing EV performance through buck‐boost converters with renewable energy integration. The proposed technique is the joined execution of Flying Foxes Optimization (FFO) and Viscoelastic Constitutive Artificial Neural Networks (vCANNs) techniques. The proposed method's goal is to enhance the energy efficiency, minimize EV charging cost, and mitigating environmental impacts. The renewable energy sources: solar panels, fuel cells, and wind turbines, are integrated into the EV power system through buck‐boost converters. The buck‐boost converter's control signal is optimized through the FFO method. vCANNs are used to predict these control parameters. The proposed strategy is executed in MATLAB software and is compared with existing strategies. In comparison with other current approaches like particle swarm optimization, heap based optimizer, and wild horse optimize, the proposed method achieves a high efficiency of 99% and low cost of 0.05 €/KWh. [ABSTRACT FROM AUTHOR]

Poland has experienced a remarkable growth in renewable energy adoption, notably in photovoltaic (PV) solar systems. The majority of installations are prosumer PV micro-installations, exceeding predicted capacity targets outlined in the Energy Policy of Poland until 2040. Despite the significant growth in installed PV capacity, there is still a lack of comprehensive research focusing on fast assessment of energy generation capacity for solar PV micro-systems. This study aims to address this gap by providing a comprehensive analysis of energy production potential across different configurations and locations in Poland. Using geocoding techniques, solar irradiation data from PVGIS database and pvlib-python library, a methodology was developed to rapidly estimate energy generation from 1 kWp solar PV systems. Results reveal spatial disparities in energy yield from solar PV micro installations in Poland, influenced by factors such as geographical location and panel orientation and inclination. Recognizing that the presented energy indicators provide valuable initial parameters for determining solar PV system power output, this data can serve as a critical reference point for stakeholders, assisting them in estimating potential energy generation capacities in different regions of Poland. [ABSTRACT FROM AUTHOR]

The impact of solar radiation and ambient temperature on solar PV energy yield and its corresponding economic implication was investigated. The electrical load assessment was done by physical inspection through periodic visits to study location. Five different scenarios were investigated for two locations - Ogun and Bayelsa States: Case I considers the PV performance based on the locations’ historical solar radiation and temperature data, Case II considers 30 % increase in the solar radiation data while the ambient temperature data remains fixed, Case III focuses on when solar radiation data is decreased by 30 % while the ambient temperature data remains constant, Case IV considers the solar radiation data remains constant while the temperature values are increased by 30 %, and Case V examined the same solar radiation values with temperature data values being decreased by 30 %. The HOMER pro was used as the implementation tool, Electrical energy yield, Unmet electric load, Net present cost, Levelized cost, and Operating cost for Cases I, II, III, IV, and V in Ota, Ogun State were as follows: 28,659 kWh/y, 4.71kWh/y, $13,537, $0.166, 271.43kWh/y; 37,260 kWh/y, 1.63kWh/y, $12,417, $0.152, 290.43kWh/y; 20,058kWh/y, 3.22kWh/y, $15,663, $0.192, 293.14kWh/y; 28,659kWh/y, 4.71kWh/y, $13,537, $0.166, 271.43kWh/y; and 28,659kWh/y, 4.61kWh/y, $13,437, $0.156, 261.43kWh/y, respectively while similar trend was observed for Otuasega in Bayelsa State. The results of the analysis showed that the optimal performance of the PV module occurred at a higher solar radiation and a lower ambient temperature.

Solar irradiance is a component of the climate system that plays a major role in the global climate change phenomenon. Top of the atmosphere (TOA) radiative flux affects the temperature of the Earth's surface and the atmosphere. In this paper, the impact of CO2 removal on radiative flux at the TOA and surface over West Africa is investigated. A decrease in TOA downwelling shortwave radiations across the periods is simulated, signifying a reduction in warming at the TOA within latitude 14°N and above. Reduction in warming is projected in Niger, Burkina Faso, and Mali in the period 2020–2049 as TOA upwelling longwave radiation increased in these locations. Significant changes are projected more under clear sky conditions than cloudy. Removal of 1.13 ppm/yr (8.81 Gigatonnes of CO 2 per year), 2.23 ppm/yr (17.39 Gigatonnes of CO 2 per year), and 3.50 ppm/yr (27.30 Gigatonnes of CO 2 per year) is projected for the periods 2020–2049, 2040–2069, and 2071–2100, respectively. Clear sky condition simulation shows a greater reduced level of warming before the end of the year 2100. Radiative flux reduction is projected far more at the surface than at TOA. Climate sensitivity and radiative forcing of −2.2 ± 0.1°CW/m2 and −0.4 ± 0.1 W/m2 are obtained, respectively. In contrast, a climate system's response to forcing of Δ T = 0.88°C and Δ T = 3.76°C per unit decrease in radiative forcing are projected on global and West African scales, respectively. Research highlights: Most radiative parameters were reduced in each period compared to the reference period under both cloudy and clear sky conditions at the TOA. Significant changes are projected more under clear sky conditions than cloudy. The projection of decreased radiative flux over West Africa is more at the surface than at TOA. Simulated impacts are from latitude 14°N and below as warming is reduced in this area, while moderate warming is expected above it. Reduction in atmospheric CO2 concentrations enhanced negative climate forcing. The implementation of carbon dioxide removal is capable of reversing climate in the long run. [ABSTRACT FROM AUTHOR]

This work examines the potential of some of the Gulf Cooperation Council countries (GCC) (Saudi Arabia (KSA), the United Arab Emirates (UAE), Qatar (QA), Bahrain (BH), Oman (OM)), Yemen (YE), Iraq (IQ), and Jordan (JO) to use their abundant solar radiation to generate electricity through PV technology. The study is structured to help decision-makers access the necessary data related to the status of solar-energy infrastructure and power production in the study region. The study investigates current efforts to establish PV technology and the challenges hindering the development of this technology. These efforts and challenges are then benchmarked against their status in Australia, which has climate and landscape conditions similar to those of the countries in the study region. It was found that Australia is successfully adopting solar energy in households and industrial locations despite its historical reliance on fossil fuels for energy production. This offers a potential avenue for replicating the Australian model of PV development in the study region. This work also addresses the effect of natural and anthropogenic aerosols on the performance of the PV panels. Meanwhile, it also proposes a conceptual model to help local governments and decision-makers in adopting solar-energy projects in the study region. Additionally, a preliminary carbon-footprint analysis of avoided emissions from PV energy utilization compared to national grid intensity was performed for each country. Findings show that the countries in the study region have great potential for using solar energy to gradually replace fossil fuels and protect the environment. It is observed that more hours of daylight and clear-to-scattered cloud coverage help increase solar irradiance near the ground all year around. Dust and aerosol loadings, however, were found to greatly reduce solar irradiance over the GCC area, especially during large dust events. Despite the high potential for harvesting solar energy in the study region, only a handful of PV plants and infrastructural facilities have been established, mostly in the KSA, the UAE, and Jordan. It was found that there is a critical need to put in place regulations, policies, and near-future vision to support solar energy generation and reduce reliance on fossil fuels for electricity production.

Various factors, such as solar radiation, the tilt angle of solar photovoltaic modules, and the conversion efficiency of these modules, play crucial roles in influencing the power output of photovoltaic (PV) systems. When it comes to predicting faults in distributed photovoltaic power sources, the primary focus and challenges revolve around the constraints imposed by maximum power. Consequently, a fault prediction method was introduced to distributed photovoltaic power sources, centered on the calculation of maximum admissible power. The methodology involved constructing an output model for distributed photovoltaic power sources to ascertain the limits of reactive power. By calculating the distribution of harmonic currents based on the maximum admissible power, the short-circuit capacity was determined. Faults in distributed photovoltaic power sources and categorized them into different fault warning levels. Through experimental analysis, it is demonstrated that the proposed method predicts photovoltaic power loss data more accurately, thus facilitating effective fault detection. Furthermore, the method computes the correlation of warning indicators for each node in the distributed photovoltaic power source, thereby establishing the warning fault status for each node. In the experiments, three nodes are in the green warning state, six in the yellow warning state, two in the orange warning state, and one in the red warning state, underscoring the favorable fault warning effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Under the action of waves, a small unmanned surface vehicle (USV) will experience continuous oscillation, significantly impacting its photovoltaic power generation system. This paper proposes a USV photovoltaic power generation simulation model, and the efficiency of photovoltaic MPPT control under wave action is studied. A simulation model for solar irradiance on solar panels of USV under wave action is established based on CFD and solar irradiation models. The dynamic changes in irradiance of USV solar panels under typical wave conditions are analyzed. The MPPT efficiency of USV photovoltaic power generation devices under continuously changing irradiance conditions is studied on this basis. The simulation research results indicate that waves and solar altitude angles significantly impact the instantaneous irradiation energy of USV photovoltaic devices. However, the impact of waves on the average irradiance is relatively tiny. The sustained oscillation of irradiance poses certain requirements for the Maximum Power Point Tracking (MPPT) control frequency of USV photovoltaic systems; a disturbance control frequency of no less than 50 Hz is proposed. [ABSTRACT FROM AUTHOR]

Summary: It is well established that solar irradiance greatly influences tree metabolism and growth through photosynthesis, but its effects acting through individual climate metrics have not yet been well quantified. Understanding these effects is crucial for assessing the impacts of climate change on forest ecosystems.To describe the effects of solar irradiance on tree growth, we installed 110 automatic dendrometers in two old‐growth mountain forest reserves in Central Europe, performed detailed terrestrial and aerial laser scanning to obtain precise tree profiles, and used these to simulate the sum of solar irradiance received by each tree on a daily basis. Generalized linear mixed‐effect models were applied to simulate the probability of growth and the growth intensity over seven growing seasons.Our results demonstrated various contrasting effects of solar irradiance on the growth of canopy trees. On the one hand, the highest daily growth rates corresponded with the highest solar irradiance potentials (i.e. the longest photoperiod). Intense solar irradiance significantly decreased tree growth, through an increase in the vapor pressure deficit. These effects were consistent for all species but had different magnitude.Tree growth is the most effective on long rainy/cloudy days with low solar irradiance. [ABSTRACT FROM AUTHOR]

Habitability at the surface of a planet depends on having an atmosphere long enough for life to develop. The loss of atmosphere to space is an important component in assessing planetary surface habitability. Current models of atmospheric escape from exoplanets are not well constrained by observations. Atmospheric escape observations from the terrestrial planets are available in public data archives. We recast oxygen escape rates from Earth derived from an instrument on Dynamics Explorer‐1 as function of solar wind and compare them to similar data from Mars. Analysis demonstrates that oxygen escape rates from Mars are not as sensitive to variations in solar power components as those from Earth. Available data from Venus can confirm or refute the assertion that oxygen escape from magnetized planets is more sensitive than that from unmagnetized planets. Plain Language Summary: Habitability of a planet depends on having an atmosphere long enough for life to develop. NASA and ESA data archives contain information about atmospheric escape from the terrestrial planets. For these planets oxygen ions dominate atmospheric escape. The data archives are just beginning to be analyzed and presented in a form that allows comparison with, and validation of, models of the interaction of stellar winds with exoplanets. We derive oxygen escape rates from Earth as a function of solar power components from a recasting of Dynamics Explorer‐1 data and compare them to similar data from Mars. Our analysis demonstrates that oxygen escape rates from Mars are not as sensitive to variations in the solar power components as those from Earth. These data and similar data from Venus will prove to be important constrains on models of stelar wind/atmosphere interactions and atmospheric escape from exoplanets. Key Points: We recast oxygen escape rates from Earth derived from an instrument on Dynamics Explorer‐1 as a function of solar energy inputsWe compare escape rates for a magnetized planet (Earth) and an unmagnetized planet (Mars) as a function of solar energy inputsOxygen escape rates from Mars are not as sensitive to variations in the solar power components as those from Earth [ABSTRACT FROM AUTHOR]

Photovoltaic solar energy is one of the renewable energy options that allows us to reduce the effects of climate change. Furthermore, it is an inexhaustible source available on the planet that contributes to sustainable development. This research adopts both a qualitative and quantitative approach, with global irradiance data having been collected during the years 2012-2021, obtaining an average of 4.19788030 W/m² along with peak solar hours, which resulted in variations in the results. Subsequently, the number of photovoltaic solar panels, the inverter, the batteries and the controller required were calculated. The scheme of a photovoltaic module for a water pumping system for crops was also designed, using the Wondershare EdrawMax software. In addition, a survey was conducted among farmers to determine the daily consumption and power of the water pump they use to irrigate crops. [ABSTRACT FROM AUTHOR]

Accurate forecasting of seasonal power consumption is crucial for effective grid management, especially with increasing energy demand and renewable energy integration. Weather patterns significantly influence energy usage, making load prediction a challenging task. This study employs machine learning algorithms, including Random Forest (RF), Artificial Neural Networks (ANN), and Decision Tree (DT) models, to forecast electricity consumption using meteorological variables such as solar irradiance, humidity, and ambient temperature. The impact of weather elements on load prediction accuracy across different seasons is explored using seasonal forecasting techniques. The results demonstrate the superior performance of ANN and RF models in forecasting summer and winter loads compared to the rainy season. This discrepancy is attributed to the abundance of data for the summer and winter seasons, and the ability of the models to capture complex patterns within the data for these particular seasons. The study highlights the potential of machine learning techniques, particularly ANN and RF, in conjunction with meteorological data analysis, for enhancing the accuracy of seasonal electrical load forecasting. This can contribute to more effective power grid management and support the transition towards a more sustainable energy landscape. The findings underscore the importance of data quality, quantity, and appropriate model selection for different seasonal conditions. [ABSTRACT FROM AUTHOR]

A new cost-effective radiometer has been designed, built, and tested to measure direct normal solar irradiance (DNI). The proposed instrument for solar irradiance measurement is based on an optical fiber as the light beam collector, a semiconductor photodiode to measure the optical power, and a calibration algorithm to convert the optical power into solar irradiance. The proposed radiometer offers the advantage of separating the measurement point, where the optical fiber collects the solar irradiation, from the place where the optical power is measured. A calibration factor is mandatory because the semiconductor photodiode is only spectrally responsive to a limited part of the spectral irradiance. Experimental tests have been conducted under different conditions to evaluate the performance of the proposed device. The measurements confirm that the proposed instrument performs similarly to the expensive high-accuracy pyrheliometer used as a reference. [ABSTRACT FROM AUTHOR]

The paper studies the electron irradiation at an energy of 30 keV affecting the diffuse-reflectance spectra and integrated absorption coefficient of solar irradiance of the micron-sized mZnO powder modified by adding nY2O3 nanoparticles in the amount of 0.1 to 10 wt.%. The best content of nanoparticles is found to be 3 wt.%, when the diffuse-reflectance spectra and the integrated absorption coefficient of the modified powder are 1.41 times lower than in the initial powder. It is shown that free electrons forming during the powder irradiation, make the highest contribution to the powder degradation. [ABSTRACT FROM AUTHOR]

Solar radiation plays a dominant role in numerous engineering systems. Shading, resulting from various objects, modulates the amount and distribution of irradiance received by surfaces. This research aimed to develop a model for assessing the spatial and temporal distribution of insolation on urban surfaces affected by building shadows. A Scilab code was implemented to compute building shadows using a direct mathematical algorithm. The results indicate that the front façade of the building is exposed to the sun for approximately 35–40% of the year's hours, collecting more than 0.5 MW-hr/m 2 annually. It was observed that building shadows effectively mitigate solar radiation, especially when the building width is between 0.25 and 3 times the spacing between buildings. The ground exhibited the lowest shading factor value and the highest sensitivity to latitude. These findings are intended to serve as a reference for urban planners. [ABSTRACT FROM AUTHOR]

This paper proposes a load flow model to estimate the actual power output by incorporating time series data for solar irradiance and wind speed at a specific location. The integration of this time series data into the network is carried out in three distinct scenarios: considering only solar output, only wind output, and the combined contribution of solar and wind. These data integration processes are followed by load flow analysis conducted on the standard IEEE 33Bus radial distribution system. The time series simulations are executed using OpenDSS (Open Distribution System Simulator) software, which utilizes a COM (Common Object Model) interface to display results in MATLAB. [ABSTRACT FROM AUTHOR]

This work examines the potential of some of the Gulf Cooperation Council countries (GCC) (Saudi Arabia (KSA), the United Arab Emirates (UAE), Qatar (QA), Bahrain (BH), Oman (OM)), Yemen (YE), Iraq (IQ), and Jordan (JO) to use their abundant solar radiation to generate electricity through PV technology. The study is structured to help decision-makers access the necessary data related to the status of solar-energy infrastructure and power production in the study region. The study investigates current efforts to establish PV technology and the challenges hindering the development of this technology. These efforts and challenges are then benchmarked against their status in Australia, which has climate and landscape conditions similar to those of the countries in the study region. It was found that Australia is successfully adopting solar energy in households and industrial locations despite its historical reliance on fossil fuels for energy production. This offers a potential avenue for replicating the Australian model of PV development in the study region. This work also addresses the effect of natural and anthropogenic aerosols on the performance of the PV panels. Meanwhile, it also proposes a conceptual model to help local governments and decision-makers in adopting solar-energy projects in the study region. Additionally, a preliminary carbon-footprint analysis of avoided emissions from PV energy utilization compared to national grid intensity was performed for each country. Findings show that the countries in the study region have great potential for using solar energy to gradually replace fossil fuels and protect the environment. It is observed that more hours of daylight and clear-to-scattered cloud coverage help increase solar irradiance near the ground all year around. Dust and aerosol loadings, however, were found to greatly reduce solar irradiance over the GCC area, especially during large dust events. Despite the high potential for harvesting solar energy in the study region, only a handful of PV plants and infrastructural facilities have been established, mostly in the KSA, the UAE, and Jordan. It was found that there is a critical need to put in place regulations, policies, and near-future vision to support solar energy generation and reduce reliance on fossil fuels for electricity production. [ABSTRACT FROM AUTHOR]

Site adaptation has become a necessary step in resource assessment for ensuring the bankability of a renewable energy project. The process involves collecting short-term observation data to correct the long-term dataset available from the satellite-derived models, which could thus provide a more accurate estimate of the solar resource data. This study aims to enhance the site-adaptation of direct normal irradiance, as its correction remains notably challenging in comparison to global horizontal irradiance due to its larger error, which is often attributed to the complexity of cloud modeling. A new methodology for site-adaptation is proposed that exploits the use of a new indicator variable that describes the correctness of sky-condition classification by the clear-sky index. This variable has dual applications within the context of site adaptation: firstly, it is employed in the two-step binning procedure subsequent to the conventional clear-sky binning during preprocessing, and secondly, it serves as an additional input feature in machine-learning-based site adaptation. The results show that the former method can reduce the mean bias error to a mere 0.4%, while the latter is better for reducing large discrepancies as shown by the lower root mean squared error. [ABSTRACT FROM AUTHOR]

Solar energy (SE) is vital for renewable energy generation, but its natural fluctuations present difficulties in maintaining grid stability and planning. Accurate forecasting of solar irradiance (SI) is essential to address these challenges. The current research presents an innovative forecasting approach named as Transformer-Infused Recurrent Neural Network (TIR) model. This model integrates a Bi-Directional Long Short-Term Memory (BiLSTM) network for encoding and a Gated Recurrent Unit (GRU) network for decoding, incorporating attention mechanisms and positional encoding. This model is proposed to enhance SI forecasting accuracy by effectively utilizing meteorological weather data, handling overfitting, and managing data outliers and data complexity. To evaluate the model’s performance, a comprehensive comparative analysis is conducted, involving five algorithms: Artificial Neural Network (ANN), BiLSTM, GRU, hybrid BiLSTM-GRU, and Transformer models. The findings indicate that employing the TIR model leads to superior accuracy in the analyzed area, achieving R2 value of 0.9983, RMSE of 0.0140, and MAE of 0.0092. This performance surpasses those of the alternative models studied. The integration of BiLSTM and GRU algorithms with the attention mechanism and positional encoding has been optimized to enhance the forecasting of SI. This approach mitigates computational dependencies and minimizes the error terms within the model.

This paper introduces a 1-D finite difference model for analyzing the temperature distribution throughout the hardening phase in concrete structures. The model accounts for the degree of hydration-dependent internal heat rate and the solar irradiance on the receiving surface of a concrete structure, allowing for more accurate predictions of early-age behavior of concrete. The research involves the development and validation of the 1-D model using temperature measurements from an on-site concrete footing. Mesh sensitivity studies were conducted to optimize grid size for thermal analysis. The paper also explores the determination of suitable 2-D width-to-height ratios for employing the 1-D model, providing practical guidelines for its application. Major findings indicate that the 1-D model can accurately predict temperature evolution in early-age concrete and offers a cost-effective alternative to more complex 2-D, 3-D finite difference, and finite element models. For preliminary analysis and quick assessments, the 1-D model is advantageous due to its computational speed, and it holds significant potential for concrete temperature control and construction optimization.

Abstract Distributed photovoltaic (PV) power plants often lack solar irradiance monitoring devices, significantly hindering crucial functions such as power forecasting, fault diagnosis, and performance calculation for distributed PV. To address this issue, a real‐time method for soft sensing solar irradiance was proposed in distributed PV. First, we investigated the typical relationship between solar irradiance, ambient temperature, and the electrical characteristics of PV cells. Based on this relationship, we utilized the small sample modeling technique of the Genetic Algorithm‐Support Vector Machine to calculate the ambient temperature. Subsequently, a solar irradiance calculation model based on the backpropagation neural network was developed, taking the PV array voltage, current, calculated ambient temperature, and power as inputs. This approach enables for the estimation of solar irradiance in distributed PV power plants through a simple and efficient calculation process. To demonstrate the reliability and flexibility of the algorithm, we conducted testing with data under various input conditions, such as different power plant configurations, and seasons, coefficient of determination for the proposed model reached 0.95. Overall, the novelty of the proposed method offers a practical solution for soft sensing of solar irradiance in PV power plants, enabling accurate performance analysis and effective operation management without hardware investment.

Satellite and in situ sensors do not observe exactly the same measurand. This introduces a mismatch between both types of measurements in the spatial or temporal. The mismatch differences can be the dominant component in their comparison, so they have to be removed for an adequate validation of satellite products. With this aim, we propose a methodology to characterize the mismatch between satellite and in situ measurements of surface solar radiation, evaluating the impact of the mismatch on validations. The Surface Solar Radiation Data Set—Heliosat (SARAH‐2) and the Baseline Surface Radiation Network are used to characterize the spatial and temporal mismatch, respectively. The mismatch differences in both domains are driven by cloud variability. At least 5 years are needed to characterize the mismatch, which is not constant throughout the year due to seasonal and diurnal cloud cover patterns. Increasing the mismatch can artificially improve the validation metrics under some circumstances, but the mismatch must be always minimized for a correct product assessment. Finally, we test two types of up‐scaling methods based on SARAH‐2 in the validation of degree‐scale products. The fully data‐driven correction removes all the mismatch effects (systematic and random) but fully propagates SARAH‐2 uncertainty to the corrections. The model‐based correction only removes the systematic mismatch difference, but it can correct measurements not covered by the high‐resolution data set and depends less SARAH‐2 uncertainty. Plain Language Summary: Satellite and in situ measurements are frequently not directly comparable due to the different temporal, spectral, or spatial extents covered by each sensor. The principle of comparing apples against apples is often violated because a mismatch exists between both types of measurements. This study proposes a methodology to estimate the mismatch between satellite and in situ measurements of solar radiation measurements. The paper fully characterizes the spatial and temporal mismatch, evaluating the impact of the mismatch on product validation. Increasing the mismatch generally worsens the validation metrics but there are some situations where validation metrics are artificially improved giving an unrealistic overview of the product performance. The study also shows how the mismatch can be corrected with high‐resolution measurements, highlighting the high sensitivity of the results obtained to the quality of the high‐resolution data. Key Points: Solar radiation mismatch is driven by cloud cover variability and changes with cloud seasonal and diurnal cycles of each siteThe mismatch can either artificially improve or worsen the validation metrics giving an unrealistic picture of product performanceUncertainty estimates are needed, but currently missing, to assess the suitability of high‐res products for up‐scaling in situ measurement [ABSTRACT FROM AUTHOR]

The phenomenon of the Gnevyshev gap was first identified in the solar-corona irradiance data (green line). Later, it was studied in the sunspot, coronal, and heliospheric data. We have investigated the Gnevyshev gap in the magnetic field data and have arrived at the conclusion that it reflects the behavior of the large-scale magnetic field. The Gnevyshev gap occurs at the polarity reversal of the solar magnetic field at the photospheric level. The presence of the Gnevyshev gap in sunspot data at the photospheric level is disguised by nonglobal structures that retain dependence on both latitude and longitude (the accepted mathematical term is tessaral; see below for more detail). However, it is clearly visible in the magnetic field data at the photospheric level and is even more pronounced at the source surface (i.e., in the corona). [ABSTRACT FROM AUTHOR]

A method for detecting clear‐sky periods from photovoltaic (PV) power measurements is presented and validated. It uses five tests dealing with parameters characterizing the connections between the measured PV power and the corresponding clear‐sky power. To estimate clear‐sky PV power, a PV model has been designed using as inputs downwelling shortwave irradiance and its direct and diffuse components received at ground level under clear‐sky conditions as well as reflectivity of the Earth's surface and extraterrestrial irradiance, altogether provided by the McClear service. In addition to McClear products, the PV model requires wind speed and temperature as inputs taken from ECMWF twentieth century reanalysis ERA5 products. The performance of the proposed method has been assessed and validated by visual inspection and compared to two well‐known algorithms identifying clear‐sky periods with broadband global and diffuse irradiance measurements on a horizontal surface. The assessment was carried out at two stations located in Finland offering collocated 1‐min PV power and broadband irradiance measurements. Overall, total agreement ranges between 84% and 97% (depending on the season) in discriminating clear‐sky and cloudy periods with respect to the two well‐known algorithms serving as reference. The disagreement fluctuating between 6% and 15%, depending on the season, primarily occurs while the PV module temperature is adequately high and/or when the sun is close to the horizon with many more interactions between the radiation, the atmosphere and the ground surface. [ABSTRACT FROM AUTHOR]

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The global pivot towards sustainable energy solutions necessitates a closer examination of green hydrogen production using renewable energy sources. This study aimed to assess the feasibility and efficiency of green hydrogen production on an industrial scale using solar and wind energy in Diyala city, Iraq. Experimental weather data, including solar irradiance, ambient temperature, and wind speed, were meticulously collected throughout 2022. The analysis indicated that, for wind energy, the optimum electrolyser capacity that matched a 1.5 MW wind turbine achieved a hydrogen production of 11,963 kg/year, with associated costs of $8.87/kg. In contrast, when focusing on solar energy, the ideal electrolyser capacity harmonizing with a 2 MW solar photovoltaic generated a notably higher hydrogen output of 94,432 kg/year at a more competitive cost of $6.33/kg. These findings underscore the potential economic advantages of solar-based green hydrogen production over wind-based methods in Diyala city. Furthermore, the significant difference in hydrogen production yields between the two methods emphasizes the need to optimize renewable infrastructure based on location-specific renewable resources. This study offers valuable insights into tailoring green hydrogen production strategies in regions with similar climatic conditions to Diyala and serves as a blueprint for future renewable energy-driven hydrogen production initiatives. [ABSTRACT FROM AUTHOR]

Solar irradiance data provided by the Copernicus program are crucial for several scientific, environmental, and energy management applications, but their validation by means of ground-based measurements may be necessary, especially if daily and hourly data resolutions are required. The validation process not only ensures that reliable information is available for solar energy resource planning, power plant performance assessment, and grid integration, but also contributes to the improvement of the Copernicus system itself. Ground-based stations offer site-specific data, allowing for comprehensive assessments of the system's performance. This work presents a comparative statistical analysis of solar irradiance data provided by the Copernicus system and ground-based measurements on a seasonal basis at three specific Italian reference sites, showing a maximum average relative error of less than 7% for hourly horizontal global irradiance in the irradiance range defined by the IEC 61724-2. [ABSTRACT FROM AUTHOR]

Aerosol optical depth (AOD) constitutes a key parameter of aerosols, providing vital information for quantifying the aerosol burden and air quality at global and regional levels. This study demonstrates a machine learning strategy for retrieving AOD under cloud-free conditions based on the synergy of machine learning algorithms (MLAs) and ground-based solar irradiance data. The performance of the proposed methodology was investigated by applying different components of solar irradiance. In particular, the use of direct instead of global irradiance as a model feature led to better performance. The MLA-based AODs were compared to reference AERONET retrievals, which encompassed RMSE values between 0.01 and 0.15, regardless of the underlying climate and aerosol environments. Among the MLAs, artificial neural networks outperformed the other algorithms in terms of RMSE at 54% of the measurement sites. The overall performance of MLA-based AODs against AERONET revealed a high coefficient of determination (R2 = 0.97), MAE of 0.01, and RMSE of 0.02. Compared to satellite (MODIS) and reanalysis (MERRA-2 and CAMSRA) data, the MLA-AOD retrievals revealed the highest accuracy at all stations. The ML-AOD retrievals have the potential to expand and complement the AOD information in non-existing timeframes when solar irradiances are available. [ABSTRACT FROM AUTHOR]

In the current scenario, renewable energy sources (RES) are used in transport applications to minimise the dependency on fossil fuels. The electrical vehicle (EV) has contributed a vital role in smart grids with the penetration of RES. The battery connected EVs have increased in the past few years owing to numerous benefits. Thus, it is necessary to develop an optimum charging controller for the charging station to meet the energy demand. In order to accomplish this goal, hybrid fuzzy fractional order proportional integral derivative (HF2OPID) is proposed. Meanwhile, the controlling parameters of the HF2OPID are tuned by all member based optimisation algorithms (AMBO). Alongside, a hybrid honey badger recurrent neural network (H2B-RNN) provides peak power from the solar photovoltaic (SPV) module. The honey badger optimisation algorithm examines the optimum weights of the layers. The proposed method is implemented on the MATLAB/Simulink platform, and the results are compared with the existing methods. The obtained results demonstrate the proposed method's compatibility with the available techniques in the literature. [ABSTRACT FROM AUTHOR]

This paper describes the control of temperature rise in photovoltaic panels by using wickless heat pipe cooling technique. These heat pipes take the help of gravity for circulation of their working fluid without using a wick structure. For current thermal management study, two solar panels of same dimensions and two identical gravity assisted wickless heat pipes are designed and fabricated. One of the panels is cooled by using these two heat pipes. Other panel, without heat pipes is used for performance comparison purpose and has been designated as the reference module. In order to have the same operating conditions, these panels have been installed together. Experimental parameters such as voltage, current from both panels, module temperatures along with solar irradiance and ambient temperature are recorded with the help of a data logger. For this study, minute wise data of various parameters have been recorded for the test period of 9 AM to 5 PM, since solar radiation intensity is high during this time period. Actual data tabulation for the analysis has been done at 15 minute intervals. The comparative analysis for each panel has been carried out for various dates and has been presented in this paper. A significant drop in temperature of around10%, corresponding to a temperature reduction of5.96°C, has been estimated in the operating temperature of photovoltaic module equipped with wickless heat pipes. Further, due to the thermal management, in this study, the optimum enhancement in photoelectric conversion efficiency is found to be 7.69%, with 15.4% of panel with cooling and 14.3% of panel without cooling. Similarly optimum improvement of 4.03% in power output has been recorded with 27.09 W from panel with cooling and 26.04 W from the panel with no cooling. The outcomes of this experimental study are highly encouraging in the field of photovoltaics. [ABSTRACT FROM AUTHOR]

Photovoltaic systems convert solar irradiance into electricity. Due to some factors, the amount of solar irradiance arriving at the solar photovoltaic collector at a specific location varies. The goal of this study was to develop a mathematical model for predicting the performance of a photovoltaic system, which depends on the amount of solar irradiance. A novel model for solar irradiance in the form of a delay differential equation is introduced by including the factor of delayed solar irradiance, hour angle and the sun’s motion. The simulation study is carried out for the three scenarios of weather conditions: a clear day, a slightly cloudy day, and a heavily overcast day. The numerical solution is obtained by adopting the 4th-order Runge Kutta method coupled with a parameter fitting technique, the Nelder Mead algorithm, which is implemented by using MATLAB software. The data from a solar plant in Pahang, Malaysia, was used for model validation and it is found that the prediction profile for solar irradiance aligns well with the intermediate and decay phases, but deviates slightly during the growth phase. The output current and power for the solar photovoltaic panel were treated as time-dependent functions. As the solar irradiance increases, the output current and power of the solar panel will increase. The result showed that the maximum output current and output power of STP250S-20/Wd crystalline solar module decreased by 42% and 76%, respectively, during slightly cloudy and heavily overcast conditions when compared to clear days. In other words, the performance of a photovoltaic module is better on clear days compared to cloudy days and heavily overcast. These findings highlight the relationship between delayed solar irradiance and the performance of the solar photovoltaic system. [ABSTRACT FROM AUTHOR]

Solar energy is used in many domestic and industrial areas. To make maximum use of solar energy, it is important to know the irradiance value per unit area. Irradiance measurements are carried out at many stations. In cases where the irradiance value is not measured, empirical models are generally used. With the widespread use of machine learning methods in prediction problems in many fields, it has gained increasing popularity recently. This study aims to predict the solar irradiance of all cities in the Mediterranean region in Turkiye using statistical method and popular machine learning methods. The performances of these models are compared. Ensemble Learning methods (Gradient Boosting, Extreme Gradient Boosting) which are among the popular machine learning methods, and Artificial Neural Networks were used. The data used belong to 8 cities in the Mediterranean Region, Türkiye. Declination angle, wind speed, ambient temperature, relative humidity, and cloudiness index were chosen as input variables. When the results of the models established for each city are examined, it is seen that the models established with machine learning methods are more successful than statistical methods. The best results were obtained from models established with the Extreme Gradient Boosting method (R2 = 0.9993, MAPE = 0.0119). [ABSTRACT FROM AUTHOR]

This study introduces a novel methodology for the accurate characterization of photovoltaic (PV) devices that are using spectral distributions from various unfiltered light sources, including ASTM G173-03 solar irradiance, xenon arc lamp, metal halide lamp and tungsten halogen lamp within the 300–1300 nm wavelength range. By leveraging experimental values of external quantum efficiencies and open circuit voltages from nine distinct solar cell technologies, the authors calculated efficiencies with minimal deviation from the experimental benchmarks. The approach uniformly applies across all light sources, revealing a significant correlation between the power and spectrum of light sources that mitigates their spectral influence on solar cell output parameters. This work not only advances the understanding of light source effects on PV device performance but also proposes a correction methodology that significantly reduces evaluation errors, providing a pathway towards more accurate and cost-effective PV device testing and characterization. [ABSTRACT FROM AUTHOR]