Solar Radiation Model Research Articles

Development of an engineering-friendly evaluation model for solar spectral irradiance using readily accessible subaerial meteorology

Solar spectral irradiance has a crucial impact on building energy conservation, especially on photovoltaic (PV) generation. However, it takes a high cost to measure and predict the dynamic solar spectral irradiance for various atmosphere conditions and sun positions. Combining with machine learning, this paper developed a novel solar spectral irradiance estimation model to evaluate the annual solar spectral property in a region. This paper employs the readily accessible subaerial meteorology as model input. The average photon energy (APE) serves as a connection between the normalized solar spectral irradiance and the meteorology parameters. Verification showed the model this paper proposed estimated the normalized solar spectral irradiance well. Further, annual simulation of solar spectral irradiance was conducted by inputting typical meteorology year (TMY) dataset. The annual difference of the normalized spectral irradiance reached to 10.57%, which reflects the great importance to determine the practical solar spectral irradiance. A typical day of spectra was proposed for each month to reveal the monthly variation in solar spectral irradiance. This study provides a convenient technical method to evaluate the solar spectral property for engineering applications. The results may guide industries in selecting suitable solar cells for the region, thereby prompting the development of solar applications.

Spatial and temporal analysis of decomposition models in China

This study presents a comprehensive evaluation of 15 decomposition models (12 empirical and 3 atmospheric transmittance models) for estimating diffuse horizontal irradiance at 17 radiation sites in China, using hourly radiation data from 2011 to 2020. Our results show distinct patterns in model performance across different geographical regions, seasons, and sky conditions. The Liu model demonstrates the best overall performance with an RMSE of 78.20 W/m2, while model accuracy shows significant geographical variation, performing best in South and Southeast China (RMSE<70 W/m2) and worst in the Qinghai-Tibet Plateau and Northwest China (RMSE>90 W/m2). Seasonal analysis reveals better performance in winter than in summer, with RMSE differences approaching 40 W/m2, mainly due to the higher proportion of solar elevation angles exceeding 30° in summer. Under different sky conditions (classified by clearness index: 0–0.35 for overcast, 0.35–0.65 for partly cloudy, 0.65–1 for clear skies), most models follow an RMSE pattern of partly cloudy > clear sky > overcast. However, the Reindl2, Boland, DIRINT, and DIRINDEX models deviate from this trend due to their formula structure and sensitivity to atmospheric parameters. To reduce these regional disparities, we propose a new region-specific model selection strategy: the DIRINDEX model for eastern regions, DIRINT for central areas, and Karatasou for western regions. This combined approach reduces the overall RMSE to 73.17 W/m2. This research deepens our understanding of the application of decomposition models in China's complex geographical and climatic conditions, offering valuable references for solar radiation modeling and renewable energy forecasting in diverse climatic regions.

GIS-based solar radiation modelling for photovoltaic potential in cities: A sensitivity analysis for the evaluation of output variability range

Currently, there is increasing attention to matching the buildings’ energy demand with energy produced by renewable sources. This approach serves to both reduce the environmental footprint, the target of several European policies and initiatives and ensure access to energy for everyone worldwide, aligning with one of the aims of the United Nations Agenda 2030. Urban environments are suitable for the installation of photovoltaic panels, which can be easily integrated within roofs to minimise land use and installation costs. Multiple studies focused on solar radiation as the most critical element affecting the estimation of the photovoltaic potential, in particular by resorting to Geographic Information Systems (GIS). Among different tools, which are useful for integrating layers with different spatial resolutions, the r.sun function embedded in GRASS GIS has been selected as the most suitable to capture complex variations resulting from weather and geographical parameters. However, a gap in the literature has emerged as no study is available to define the precision degree of the tool when inputting parameters derived from various data sources based on different model assumptions. The scope of this research is to collect open-access weather inputs and quantify the solar radiation output along with its variability range. Based on 28 test plans, resulting from the combination of input parameters from alternative data sources, this research quantified the output variability range to be ±20 %, thus requiring additional processing to increase the precision. The paper concludes with a critical assessment of the strengths and weaknesses of the current research, emphasizing the importance of validating the results with measured values in forthcoming research, to move from precision to accuracy evaluation.

An Assessment of the Weather Research and Forecasting Model for Solar Irradiance Forecasting under the Influence of Cold Fronts in a Desert in Northwestern Mexico

Northwestern Mexico has a desert climate with high solar resources. Clear skies and low humidity during most of the year favor their use. In winter, the arrival of cold air masses from the polar latitudes cause instability and abrupt changes in atmospheric variables, increasing the error of short-term forecasts. This work focuses on the evaluation of the Weather Research and Forecasting (WRF) model for predicting the global horizontal irradiance (GHI), considering different parameterizations of shortwave and longwave solar radiation during the influence of five cold fronts that affected the desert region of northwestern Mexico. The simulation was carried out under four main shortwave configurations and the results were evaluated with surface measurements and compared with climate information from NASA-POWER. The GHI predicted with the Dudhia parameterization showed an overestimation of the WRF model during most of the analyzed events; the most accurate predictions obtained correlation values between 0.85 and 0.91 and a mean absolute error between 15 and 45 W m−2. In periods where intermittent clouds prevailed, the mean error increased by almost 20%. An evaluation of the different proposed configurations shows advantages with the shortwave Dudhia and longwave RRTM parameterizations, providing a useful meteorological tool for predicting short-range variations in the GHI to improve the operability of solar power generation systems.

Impacts of Extreme Ultraviolet Late Phase of the Solar Flare on Ionospheric Electrodynamics

Previous investigations of ionospheric electrodynamical responses to solar flares primarily focused on the main phases (MPs) of solar flares. Typical solar irradiance models for driving global ionosphere models do not include the extreme ultraviolet (EUV) late phase (ELP) of flares, which was recently observed with new high-quality solar EUV spectra. Thus, it is still unclear how ionospheric electrodynamics respond to the flare ELP. Here, we analyzed the ionospheric electrodynamical response to the MP and ELP of the X9.3 flare on 2017 September 6, using observations from ground magnetometers, along with simulation results from an ionosphere–thermosphere coupled model. Observations indicated an intensification of the dayside eastward equatorial electrojet (EEJ) by approximately 12 nT at the ELP peak as compared to the quiet day reference. Additionally, the dayside eastward electric field increased due to the ELP, which is different from the reduction of dayside electric fields during MP. The upward E × B plasma drifts decreased by 2.5 m s–1 during MP but increased by 0.75 m s–1 during the ELP. Altitude-dependent responses of ionospheric conductivities to the ELP modulated the relative contribution of the E- and F-region wind dynamo to zonal electric fields, resulting in an overall increase in the daytime eastward electric fields. Furthermore, combined effects of electric fields and conductivities enhancements contributed to EEJ intensification during the ELP. This study enhances our understanding of how solar flares with ELP change global ionospheric electric fields and currents.

Visible-light-driven selective cleavage of lignin C-C bonds on the TiO2@g-C3N4 heterostructured photocatalyst.

The selective cleavage of lignin C-C bonds is a highly sought-after process with the goal of obtaining low-molecular-weight aromatic chemicals from renewable resources. However, it remains a challenging task to achieve under mild conditions. Photocatalysis is a potentially promising approach to address this issue, but the development of efficient photocatalysts is still in progress. In this study, we introduce the heterostructured TiO2@g-C3N4 photocatalyst for the development of a visible light photocatalytic procedure for the selective cleavage of lignin C-C bonds under mild conditions. The photocatalyst displays favourable visible light absorption, efficient charge separation efficiency, and promising reusability. A typical β-O-4 dimer model, 2-phenoxy-1-phenylethanol, was effectively (96.0% conversion) and selectively (95.0 selectivity) cleaved under visible light at ambient conditions. This photocatalytic procedure was also effective when subjected to solar irradiation or other lignin dimer models with β-O-4 or β-1 linkages. This reaction occurred through a Cβ-centred radical intermediate and a six-membered transition state with photogenerated holes as the primary active species. The Cα-OH oxidative dehydrogenation of the substrate could also take place but was a relatively minor route. This study provides a new photocatalytic procedure for visible-light-driven lignin valorisation and sheds light on the design of high-performance nanocomposite photocatalysts for C-C bond cleavage.&#xD.

Design-Optimization of Solar-Collector Tilt Angle for Annual-Maximum and Seasonal-Balanced Energy Received

Abstract A detailed research study was performed on the annual energy received by a fixed-tilt solar collector. A new clear-sky model of solar radiation was developed by combining existing clear-sky models. Then, this model, was used to perform optimizations with two different objectives. The first objective is to achieve the maximum-annual solar energy received by a solar panel, while the second is to balance its energy received so that it is more uniform over the course of a year. Each of these objectives have differing optimum tilt values, depending on the location and usage of a solar receiver system. The novelty of this investigation is the use of the aforementioned solar model with a unique algorithm for multiple optimizations for these two different objectives, which results in two different optimum tilt angles that are in turn different from the widely accepted rule-of-thumb that recommends an angle that is equal to the latitude. The solar radiation results obtained with the model used herein were experimentally confirmed through comparison with measured weather data for a number of locations with different sky clearness indexes as well as comparison with those of other studies found in the literature. A major contribution of this study are the plots and tables showing optimum tilt angles as estimated by the model as well as with empirical data. By selecting the appropriate tilt angle, a designer can now decide to either maximize annual electrical energy production or else to balance delivery of electricity over the year.

Estimation of Aerosol Characteristics from Broadband Solar Radiation Measurements Carried Out in Southern Algeria

Aerosols in the atmosphere significantly reduce the solar radiation reaching the Earth’s surface through scattering and absorption processes. Knowing their properties becomes essential when we are interested in measuring solar radiation at a given location on the ground. The commonly used parameters that characterize their effects are the Aerosol Optical Depth τ, the Angstrom exponent α, and the Angstrom coefficient β. One method for estimating these parameters is to fit ground-based measurements of clear-sky direct solar radiation using a model on which it depends. However, the choice of model depends on its suitability to the atmospheric conditions of the site considered. Eleven empirical solar radiation models depending on α and β were thus chosen and tested with solar radiation measurements recorded between 2005 and 2014 in Tamanrasset in southern Algeria. The results obtained were compared to measurements made with the AERONET solar photometer on the same site during the same period. Among the 11 models chosen, the best performing ones are REST2 and CPCR2. They proved to be the best suited to estimate β with approximately the same RMSE of 0.05 and a correlation coefficient R with respect to AERONET of 0.95. The results also highlighted good performances of these models for the estimation of τ with an RMSE of 0.05 and 0.04, and an R of 0.95 and 0.96, respectively. The values of α obtained from the fitting of these models were, however, less good, with R around 0.38. Additional treatments based on a Recurrent Neural Network (RNN) were necessary to improve its estimation. They provided promising results showing a significant improvement in α estimates with R reaching 0.7 when referring to AERONET data. Furthermore, this parameter made it possible to identify different types of aerosols in Tamanrasset such as the presence of maritime, dust, and mixed aerosols representing, respectively, 31.21%, 3.25%, and 65.54%, proportions calculated over the entire period studied. The seasonal analysis showed that maritime aerosols are predominant in the winter in Tamanrasset but decrease with the seasons to reach a minimum in the summer (JJA). Dust aerosols appear in February and persist mainly in the spring (MAM) and summer (JJA), then disappear in September. These results are also consistent with those obtained from AERONET.

A stacked LSTM model for day-ahead solar irradiance forecasting under tropical seasons in Java-Bali

A stacked LSTM model for day-ahead solar irradiance forecasting under tropical seasons in Java-Bali

Artificial Intelligence-Based Improvement of Empirical Methods for Accurate Global Solar Radiation Forecast: Development and Comparative Analysis

Artificial intelligence (AI) technology has expanded its potential in environmental and renewable energy applications, particularly in the use of artificial neural networks (ANNs) as the most widely used technique. To address the shortage of solar measurement in various places worldwide, several solar radiation methods have been developed to forecast global solar radiation (GSR). With this consideration, this study aims to develop temperature-based GSR models using a commonly utilized approach in machine learning techniques, ANNs, to predict GSR using just temperature data. It also compares the performance of these models to the commonly used empirical technique. Additionally, it develops precise GSR models for five new sites and the entire region, which currently lacks AI-based models despite the presence of proposed solar energy plants in the area. The study also examines the impact of varying lengths of validation datasets on solar radiation models’ prediction and accuracy, which has received little attention. Furthermore, it investigates different ANN architectures for GSR estimation and introduces a comprehensive comparative study. The findings indicate that the most advanced models of both methods accurately predict GSR, with coefficient of determination, R2, values ranging from 96% to 98%. Moreover, the local and general formulas of the empirical model exhibit comparable performance at non-coastal sites. Conversely, the local and general ANN-based models perform almost identically, with a high ability to forecast GSR in any location, even during the winter months. Additionally, ANN architectures with fewer neurons in their single hidden layer generally outperform those with more. Furthermore, the efficacy and precision of the models, particularly ANN-based ones, are minimally impacted by the size of the validation data sets. This study also reveals that the performance of the empirical models was significantly influenced by weather conditions such as clouds and rain, especially at coastal sites. In contrast, the ANN-based models were less impacted by such weather variations, with a performance that was approximately 7% better than the empirical ones at coastal sites. The best-developed models, particularly the ANN-based models, are thus highly recommended. They enable the precise and rapid forecast of GSR, which is useful in the design and performance evaluation of various solar applications, with the temperature data continuously and easily recorded for various purposes.

Evaluating annual thermal discomfort time ratio of indoor occupants caused by solar radiation using a novel model

Thermal comfort of indoor occupants exposed to solar radiation is receiving widespread attention. Researchers have proposed many models to predict solar radiation and related indexes are used to evaluate thermal comfort. However, there are some limitations in the existing solar radiation models and evaluation indexes, such as only applying to sunny weather and requiring intensive modeling work. This study adopts a mathematical model called the improved HNU Solar Model and proposes a new evaluation index called the annual thermal discomfort time ratio by solar radiation (ratiotd, solar) to evaluate thermal comfort of indoor occupants exposed to solar radiation. The effects of different window parameters, i.e. window direction, window transmittance (Tsol) and window-to-wall ratio (WWR) on ratiotd, solar were also analyzed. The results show that the indoor area less than 2.0 m away from the window is easy to have solar discomfort. And for every 0.1 reduction in WWR, the average values of the four directions are reduced by 3% to 4%; and for every 0.1 reduction in Tsol, the ratiotd, solar values of four window directions are reduced by 4% to 6%. This study provides references for evaluating and optimizing the window design to create thermally comfortable environments for indoor occupants.

Modeling of solar radiation and sub-canopy light regime on forest inventory plots of mixed conifer and deciduous temperate forests using point clouds from personal laser scanning

Solar radiation is a major driver of forest ecosystems and affects the hydrological cycle and energy balance. The vitality of trees strongly depends on the amount of light input to individual trees, and successful forest regeneration demands a certain level of solar radiation. Therefore, characterization of the light regime inside a plant canopy (i.e., quantification and timing of light penetration) is of particular importance. Because these parameters greatly depend on the 3D structure of the forest canopy, Light Detection and Ranging (LiDAR) systems provide suitable technology for solving this task. In this study, an algorithm was developed to estimate the amount of potential solar radiation reaching the forest floor from a point cloud collected in a temperate mixed forest located in Lanzenkirchen (Lower Austria) using terrestrial LiDAR. The path length through the canopy was calculated for 40 forest inventory plots as the distance between the first and last canopy hit along a sun ray, that is, for each day during the vegetation period at an hourly resolution. This distance was easily derived from the z-coordinates of the point cloud after transforming the latter into a coordinate system in which the z-axis ran parallel to the sun rays. Applying Beer’s law, the amount of solar radiation on the forest floor was expressed as a function of the solar radiation above the canopy and the path length through the canopy. To validate the results, we used two sets of reference data: (1) hemispherical photographs taken at the same plots, using a camera on a self-levelling mount, which were processed with the HemiView software, and (2) data from a Solariscope survey. Both the Solariscope and HemiView software calculate below-canopy radiation as a percentage of above-canopy radiation, joining direct and diffuse radiation (Total Site Factor, TSF). The TSF values obtained from our modeling procedure were consistent with the Solariscope (R²=0.61) and HemiView (R²=0.71) results for the entire growing season. Because modern LiDAR-based forest inventories provide all the necessary data for our light modeling approach (the point cloud, the geographical position, and a record of the occurring tree species) without additional requirements, together with the easy applicability of the algorithm, this tool is a promising asset for ecological and silvicultural activities.

Research on Photovoltaic Power Generation Characteristics of Small Ocean Observation Unmanned Surface Vehicles

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.

Performance Comparison of Empirical Models for Estimating Global Solar Irradiation in the Soudano-Sahelian Zone of Cameroon: The Case of the City of Maroua and Garoua

The main objective of this study is to compare thirty-five (35) solar radiation models available in the open literature in order to predict monthly solar radiation in two main cities of Cameroon. This estimation and comparison are based on selected statistical comparison parameters named, root mean square error (RMSE), mean bias error (MBE), mean percentage error (MPE) and determination coefficient (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;). These different models are implemented using regression analysis tools named Exel and MATLAB. Estimated values were compared with measured values according to normalized values of statistical parameters, using measured meteorological data of more than 19 years, from 1984 to 2015. All the models have been classified with their associated ranking according to their statistical parameter accuracy. From this study it appears that the models of Ertekin and Yaldiz (MOD20), Togrul and Onat (MOD28), are more accurate than other models. Indeed, for the city of Maroua (MBE%=-2.82E-14; RMSE%=0.862; MPE=-0.00845; R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=0.985), while for Garoua (MBE%=-9.21E-15; RMSE%=0.806; MPE=-0.00631; R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=0.959). according to their accuracy these models can be therefore be used to predict monthly solar radiation for soudano-sahelian regions of Cameroon. Correlation equations found in this paper will help solar energy researcher to estimate data with trust because of its fine agreement with the observed one. hence the models presented in this study could be used to evaluate accurately the solar radiation at any locations with similar climate.

Mathematical modeling and seasonal solar radiation variability in Nigeria’s geo-political zones: A recurrence and multifractal analysis

This article delves into the seasonal variation of solar radiation patterns across Nigeria’s four geo-political zones, exploring their complex, scale-dependent behaviors. By employing chaotic quantifiers, the study characterizes irregular and self-similar patterns, enhancing our understanding of solar radiation variability and heterogeneity. The research uniquely fits meteorological parameters onto a global solar radiation model and focuses on the underexplored nonlinear aspects within tropical regions, specifically in the Nigerian context. Utilizing daily data from ERA INTERIM satellite archives for representative stations, the study employs nonlinear time series analysis methods like recurrence plots, recurrence qualitative analysis, and multifractal spectral analysis to comprehensively explore the unpredictable behaviors observed. The quantifier spectrums play a key role in revealing intricate scaling behaviors and correlation structures among environmental variables, shedding light on patterns often concealed by conventional statistical methods. For instance, we found that solar radiation variability in the northern region increases more significantly during the dry season compared to the wet season, unlike in the southern region. Additionally, the multifractal spectral analysis revealed a higher degree of complexity in solar radiation patterns during transition periods between seasons. The findings reveal a low recurrence quantitative analysis, long right tail, and truncations at both ends of the spectrum. This suggests instability in solar radiation across different seasons and locations. Nonetheless, the results also demonstrate that solar radiation is consistently available throughout the year, which is typical of tropical regions.

Analysis of Thermal and Dielectric Loss Features of Lunar Regolith Considering Real‐Time Effect Solar Irradiance

AbstractSolar irradiance received at the lunar surface is crucial for interpreting brightness temperatures detected by orbiters and for understanding the thermal, physical, and dielectric properties of the lunar regolith. We developed a real‐time effect solar irradiance (ESI) model that accounts for the influence of surface relief and terrain shading. This model was integrated with a standard thermal model to examine ESI fluctuations and their impacts on the diurnal physical temperature variations. To assess the effects of spatial resolution, we selected four locations with significant ESI disparities for simulation, then compared lunar surface temperatures at various spatial scales, ranging from 20 m to 25 km. Utilizing brightness temperature data obtained from the Chang'E‐2 (CE‐2) microwave radiometer (MRM), we integrated the shallow physical temperature profiles with the radiative transfer equation to simulate brightness temperatures and determine dielectric loss at different frequencies. In the Von Kármán crater, the received ESI exhibits a cyclical pattern of approximately 18 years and areas with rugged topography may exhibit larger ESI variations (∼7%). We found that the spatial resolution of ESI has a minimal effect on the physical and brightness temperatures at resolutions of 10 km or coarser. At the shallow layer, the average dielectric loss values are 0.0128–0.0170, 0.0083–0.0110, 0.0055–0.0073, and 0.0061–0.0081 for the CE‐2 frequencies of 3, 7.8, 19.35, and 37 GHz, respectively. The integration of real‐time ESI modeling, thermal dynamics, radiative transfer equations, and observational data enhances our comprehension of the physical temperature profile and thermal characteristics of shallow regolith.

A Comparative Study of Data Mining Methods for Solar Radiation and Temperature Forecasting Models

Photovoltaic (PV) energy systems are a leading type of renewable energy systems globally. Predicting PV energy production accurately is crucial for maintaining efficient energy grids, making informed decisions in the energy market, and reducing maintenance costs. To ensure high accuracy and optimal production, it is essential to monitor and analyze these variables regularly. Solar radiation and temperature are two meteorological variables that directly affect the quantity of PV energy generated in PV facilities. The Performance Ratio (PR) is a critical parameter for assessing PV plant performance. A comprehensive model was constructed in this study to forecast solar radiation and temperature using multiple machine learning methods, including Instance-Based K-Nearest Neighbor Algorithm (IBK), Linear Regression, Random Forests, Random Tree, Multilayer Perceptron (MLP), and MLP Regression. Moreover, we used time series approaches, such as Simple Exponential Smoothing (SES), Error-Trend-Seasonality (ETS), Autoregressive Integrated Moving Average (ARIMA) and Holt Winter&amp;#39;s Seasonal Method (HWES) models for PV systems prediction. Initially, we conducted daily forecasts as well as 1-step ahead forecasts at 5-minute intervals for both solar radiation and temperature. It is crucial to subject both variables to the same methodology in order to construct precise models for forecasting PV. Secondly, we compared the predicted values of solar radiation and temperature with the actual energy yield of the power plant to calculate energy production. Subsequently, a relative analysis of data mining models and time series models have been performed depending on the statistical error criteria like RMSE, MAPE, MABE, MAE, MSE, and direction accuracy (DAC).&amp;nbsp;

Editorial

Dear Readers, It gives me great pleasure to announce the sixth regular issue of 2024. In this issue, 6 papers cover various topical aspects of computer science by 19 authors from 7 countries: Brazil, Chile, France, India, Saudi Arabia, Tunisia, and Turkey. In an ongoing effort to further strengthen our journal, I would like to expand the editorial board: If you are a tenured associate professor or above with a strong publication record, you are welcome to apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and trends. As always, I would like to thank all authors for their sound research and the editorial board and our guest reviewers for their extremely valuable review effort and suggestions for improvement. These contributions, together with the generous support of the consortium members, help to maintain the quality of our journal.&amp;nbsp; In this regular issue, I am very pleased to introduce the following 6 accepted articles: George Marsicano, Edna Dias Canedo, Glauco V. Pedrosa, Cristiane S. Ramos, and Rejane M. da C. Figueiredo from Brazil look in their study into digital transformation of public services in a startup-based environment by 23 focus groups and 175 participants in total. Mauricio Solar and Pablo Aguirre from Chile discuss their research on 3D chest CT processes applying a ResNet-50 model to which a new dimension of information has been added, namely a simple autoencoder. In a collaborative work between researchers from Tunisia and France, Rakia Saidi, Fethi Jarray, and Didier Schwab propose in their article a cross-encoder neural network (Cross-BERT-GRU) to deal with the semantic similarity of Arabic sentences that benefits from both the strong contextual understanding of BERT and the sequential modeling capabilities of GRU. Also in a collaborative research between institutions from Tunisia and Saudi Arabia, Nozha Jlidi, Sameh Kouni, Olfa Jemai, and Tahani Bouchrika present their research on MediaPipe with GNN for human activity recognition. G.V.Vidya Lakshmi and S. Gopikrishnan from India look into missing values research for the IoT domain and in particular present IMD-MP technique that improves imputation accuracy for big data analysis in IoT applications based on spatial-temporal correlations. Last but not least, F. Didem Alay, Nagehan &amp;#304;lhan, and M. Tahir G&amp;uuml;ll&amp;uuml;o&amp;#287;lu address in their article a comparative study of data mining methods for solar radiation and temperature forecasting models. Enjoy Reading Cordially,&amp;nbsp; Christian G&amp;uuml;tl, Managing Editor Graz University of Technology, Graz, Austria

Numerical analysis of an innovative solar collector utilizing bistable composite laminates and macro fiber composite actuators

Innovative designs for solar collectors have attracted substantial academic and industrial interests owing to the high efficiency of solar panels in the recent past. This paper proposes the analysis and numerical design of a novel solar collector model composed of bistable laminates bonded with Macro Fiber Composite (MFC) actuators and solar cells. The bistable cross-ply laminate serves as the steering mechanism in this design by providing multiple stable shapes for the innovative solar collector design. Bistable morphing structures have received growing interest in aerospace structures and wind turbines due to their rapid shape-changing ability in response to changes in operating conditions, and this paper aims to integrate them more into the solar energy sector. The snap-through and snap-back motion of bistable laminates can be controlled by the voltage inputs of MFC actuators. The change of bistable laminate shape holds the solar cell in a position approximately at the latitude angle of the place to capture maximum solar energy. A systematic finite element parametric study has been performed to identify the optimum size and location of MFC actuators to achieve an energy-efficient snap-through and snap-back transition. As a result, a numerical design of a solar collector with six bistable elements has been proposed. In order to check the feasibility of the proposed designs, a numerical study has been performed to evaluate the total energy output and to optimize the solar panel tilt angle. ASHRAE’s solar irradiation model proposed in the literature has been used to identify the optimum solar panel tilt angle for maximum annual solar irradiation. This innovative solar collector model has demonstrated promising results, holds the potential for widespread application across various engineering domains, and paves the way for increased adoption of intelligent structures in everyday life.

Comparative simulation of transpiration and cooling impacts by porous canopies of shrubs and trees

Vegetation cooling effects can effectively improve the outdoor microclimate. To explore the potential of vegetation transpiration cooling, we validated the feasibility of a new coupled porous medium and solar radiation model to simulate the interactions between vegetation transpiration and other physical processes in CFD. To emphasize the spatial extent of vegetation's influence, we compared and quantified the flow-adjustment distances(LFA) for wind speed, the temperature difference between simulated temperature and background temperature(ΔT(°C)), and water vapor mass fraction(ΔMw(g/kg)) between shrub(100m) and tree(200m) canopies: 1)For both vegetations, the LFA required for ΔT and ΔMw exceeds that of wind speed achieve fully-developed, indicating the momentum adjustment occurs more rapidly than energy and mass transport processes. 2)Vegetation drag effect depends on both leaf area density and vegetation canopy's height and width.3)Vegetation exhibits a significantly higher transpiration cooling effect under ambient relative humidity(RH)=0% than RH=60%(the mean value of ΔT, ΔTfd), for the tree(z=1.5m): ΔT fd is -5.4°C (-1.4∼-1.5°C) at RH=0%/60%. 4)At RH=60%, both shrubs and trees exhibit a notable warming phenomenon near the vegetation canopies tops(ΔTMax=1.3°C/0.9°C for shrubs/trees). This study provides and confirms a reliable numerical method for simulating the interactions of vegetation transpiration and other physical processes in urban or rural areas.