Influence of building spatial patterns on wind environment and air pollution dispersion inside an industrial park based on CFD simulation.

The "spatial pattern-wind environment-air pollution" within building clusters is closely interconnected, where different spatial pattern parameters may have varying degrees of impact on the wind environment and pollutant dispersion. Due to the complex spatial structure within industrial parks, this complexity may lead to the accumulation and retention of air pollutants within the parks. Therefore, to alleviate the air pollution situation in industrial parks in China and achieve the circular transformation and construction of parks, this study takes Hefei Circular Economy Demonstration Park as the research object. The microscale Fluent model in computational fluid dynamics (CFD) is used to finely simulate the wind flow field and the diffusion process of pollutants within the park. The study analyzes the triad relationship and influence mechanism of "spatial pattern-wind environment-air pollution" within the park and studies the influence of different spatial pattern parameters on the migration and diffusion of pollutants. The results show a significant negative correlation between the content of pollutants and wind speed inside the industrial park. The better the wind conditions, the higher the air quality. The spatial morphology parameters of the building complex are the main influences on the condition of its internal wind environment. Building coverage ratio and degree of enclosure have a significant negative correlation with wind conditions. Maintaining them near 0.23 and 0.37, respectively, is favorable to the quality of the surrounding environment. Moreover, the average height of the building is positively correlated with the wind environment condition. The rate of transport and dissipation of pollutants gradually increases as the average building height reaches 16 m. Therefore, a reasonable building planning strategy and arrangement layout can effectively improve the wind environment condition inside the park, thus alleviating the pollutant retention situation. The obtained results serve as a theoretical foundation for optimizing morphological structure design within urban industrial parks.

Shifting from techno-economic to socio-ecological priorities: Incorporating landscape preferences and ecosystem services into the siting of renewable energy infrastructure.

This study examines the siting scenarios for renewable energy installations (REI) in a mountainous region of Europe (Switzerland), incorporating the external costs of ecosystem services and, innovatively, social preferences. This approach challenges the prevalent techno-economic siting paradigm, which often overlooks these externalities. To minimize the external costs of the scenarios while maximizing energy yield, Marxan, an optimization software, was employed. The energy target for all scenarios is set at 25 TWh/a, stemming from the energy gap anticipated due to the phase-out of Swiss nuclear reactors by 2050. This target is met using renewable energy infrastructure such as wind, roof-mounted photovoltaic, and ground-mounted photovoltaic systems. By integrating social preferences into the optimization, this study showcases a promising implementation that transcends the software's intended applications. It complements techno-economic approaches and offers alternative decision-making avenues. The conventional "roof first" strategy proved ineffective in preventing extensive land use for the development of new renewable energy infrastructure. Strategies incorporating ground-mounted photovoltaic infrastructure were more spatially, ecologically, and socially efficient than those without. The strategy optimized for energy yield exhibited the highest spatial efficiency but incurred significant ecosystem service costs and, surprisingly, had low social costs. In contrast, the strategy prioritizing ecosystem services was the most efficient in terms of ecosystem service costs but had elevated social costs and was spatially less efficient than other strategies. The strategy optimized for social preferences incurred the lowest social costs and excelled in spatial efficiency and ecosystem service costs. Notably, this strategy employed a limited number of planning units linked to both high ecosystem service and social costs. The findings underscore that incorporating social preferences significantly enhances the evaluation of siting options. This inclusion allows for the social acceptance of investments to be factored into costs, facilitating more informed and inclusive decisions.

Improving new energy subsidy efficiency considering learning effect: A case study on wind power.

Subsidy policies are instrumental in driving the development of new energy. However, the effective allocation of new energy subsidies over time is challenging given fiscal constraints. This study addresses this challenge by considering the learning effect associated with the new energy industry. A two-stage dynamic programming model is proposed to capture the investment decision-making process of companies under new energy subsidy policies and government subsidy setups. Theoretical findings suggest that company investment decisions in new energy are influenced by a guiding principle: The subsidy rate should be negatively correlated with the variation rate of production scale increment (VRPSI). We calibrate this investment decision principle using wind power data from 14 countries. According to this principle, excessive subsidy rates may result in a low VRPSI, thereby diminishing future investment profitability in the new energy industry and leading to subsidy inefficiency. Upon investigating the efficiency of annual subsidy allocation, we find that the subsidy rates were potentially set too high in 2014, 2016, and 2017. Furthermore, the government should exercise caution regarding an inefficient subsidy pattern whereby companies invest in new energy only when the subsidy rate exceeds a certain threshold, neglecting traditional power sources. It is crucial to note that although this study uses wind power industry data for calibration and simulation, the theoretical model can be broadly applied to other new energy industries and emerging industries with increasing marginal net profit.

Risk assessment of deep-sea floating offshore wind power projects based on hesitant fuzzy linguistic term set considering trust relationship among experts.

The development of deep-sea floating offshore wind power (FOWP) is the key to fully utilizing water resources to enhance wind resources in the years ahead, and then the project is still in its initial stage, and identifying risks is a crucial step before promoting a significant undertaking. This paper proposes a framework for identifying risks in deep-sea FOWP projects. First, this paper identifies 16 risk criteria and divides them into 5 groups to establish a criteria system. Second, hesitant fuzzy linguistic term set (HFLTS) and triangular fuzzy number (TFN) are utilized to gather and describe the criterion data to ensure the robustness and completeness of the criterion data. Third, extending the method for removal effects of criteria (MEREC) to the HFLTS environment through the conversion of TFNs, under the influence of subjective preference and objective fairness, a weighting method combining analytic network process (ANP) and MEREC is utilized to calculate criteria weights, and the trust relationship and consistency between experts are used to calculate the expert weights to avoid the subjective weighting given by experts arbitrariness. Fourth, the study's findings indicated that the overall risk level of the deep-sea FOWP projects is "medium." Fifth, sensitivity and comparative analyses were conducted to test the reliability of the assessment outcomes. lastly, this research proposes risk management measures for the deep-sea FOWP project's establishment from economic, policy, technology, environment, and management aspects.

Achieving Decentralized, Electrified, and Decarbonized Ammonia Production.

The rapid reduction in the cost of renewable energy has motivated the transition from carbon-intensive chemical manufacturing to renewable, electrified, and decarbonized technologies. Although electrified chemical manufacturing technologies differ greatly, the feasibility of each electrified approach is largely related to the energy efficiency and capital cost of the system. Here, we examine the feasibility of ammonia production systems driven by wind and photovoltaic energy. We identify the optimal regions where wind and photovoltaic electricity production may be able to meet the local demand for ammonia-based fertilizers and set technology targets for electrified ammonia production. To compete with the methane-fed Haber-Bosch process, electrified ammonia production must reach energy efficiencies of above 20% for high natural gas prices and 70% for low natural gas prices. To account for growing concerns regarding access to water, geospatial optimization considers water stress caused by new ammonia facilities, and recommendations ensure that the identified regions do not experience an increase in water stress. Reducing water stress by 99% increases costs by only 1.4%. Furthermore, a movement toward a more decentralized ammonia supply chain driven by wind and photovoltaic electricity can reduce the transportation distance for ammonia by up to 76% while increasing production costs by 18%.

Revisiting the contribution of different factors in determining the changes in potential evapotranspiration over China.

In this paper, a daily gridded observation data across China from 1961 to 2022 were used to calculate daily potential evapotranspiration (PET). The observed variables included daily temperature, sunshine hours, average wind speed, and average relative humidity. PET was determined using the Penman-Monteith method recommended by the Food and Agriculture Organization (FAO). The long-term trend of PET was investigated in six regions of China during different seasons. To further compressed the influence of various meteorological factors on the PET trend, the contribution of each meteorological element to the long-term trend of PET was analyzed. The results indicate the following: (1) PET reaches its peak during summer which values from 145 to 640 mm, while it is lowest during winter from 21 to 244 mm. (2) The spatial patterns of PET trend changes are relatively similar across the four seasons, characterized by a decrease in the eastern regions and an increase in the western regions. The reduction is most significant during the summer and the range of trend is from -2.04 to 1.48 mm/day, while the increase becomes more pronounced in the winter which trend is from -0.34 to 0.53 mm/day. (3) The contribution of factors varies significantly across different regions. In spring and autumn, RH and U have little difference in contribution from other factors. But tsun is varies different from regions, the contribution value is largest in the northwest and smallest in the northeast. However, during summer, tsun become the most significant contributor in the YZ and SE regions, while in winter, Tm emerges as the most significant contributor to the PET trend in all six subregions. In SW, the contribution from U2 is the smallest in all seasons, with RH and Tm being the two crucial factors determining the PET trend in this region.

[Analysis of the Spatiotemporal Distribution of Algal Blooms and Its Driving Factors in Chaohu Lake Based on Multi-source Datasets].

Eutrophication and harmful algae blooms are one of the common ecological and environmental problems faced by freshwater lakes all over the world. As a typical inland freshwater lake, Chaohu Lake exhibits a high level of eutrophication and algae blooms year-round and shows a spatiotemporal difference in different regions of the lake. In order to understand the basic regularity of the development and outbreak of algal blooms in Chaohu Lake, the data from the comprehensive water observation platform and remote sensing were integrated to obtain the spatiotemporal distribution of algal blooms from 2015 to 2020. Then, an evaluation model based on Boosted Regression Trees (BRT) was constructed to quantitatively assess the importance and interactions of various environmental factors on algal blooms at different stages. The results indicated that:① The occurrence of algal blooms in Chaohu Lake exhibited significant seasonal variations, with the cyanobacteria beginning to recover in spring and bring about a light degree of algal blooms in the western and coastal areas of Chaohu Lake. The density of cyanobacteria reached its maximum in summer and autumn, accompanied by moderate and severe degrees of algal bloom outbreaks. ② During the non-outbreak period, the variation in the cyanobacteria density was greatly affected by physical and chemical factors, which explained 80.3% of the variance in the change in cyanobacteria density. The high concentrations of dissolved oxygen content in the water column and the weak alkalinity (7.2-7.6) and appropriate water temperature (about 3℃) provided a favorable environmental condition for the breeding and growth of cyanobacteria. In addition, the onset of algal blooms was closely related to the air temperature steadily passing through the threshold. According to the statistics, the date of first outbreak of algal blooms in Chaohu Lake was 11 days or so after the air temperature steadily remained above 7℃. ③ During the outbreak period, the occurrence of algal blooms was influenced by the combination of cyanobacterial biomass and meteorological conditions such as temperature, wind speed, and sunshine duration. The cumulative contribution ratio of the four factors was as high as 95%, and each factor had an optimal interval conductive to the outbreak of algal blooms. Furthermore, the results of multi-factor interaction analysis indicated a larger probability of the outbreak of algal blooms in Chaohu Lake under the combined effect of high cyanobacteria density, suitable temperature, and the breeze. This study analyzed and revealed the spatiotemporal characteristics and the dominant influencing factors of algal blooms in Chaohu Lake at different stages, which could provide the scientific basis for the prediction, early warning, and disposal of algal blooms under the context of climate change.

The genomic history and global migration of a windborne pest.

Many insect pests, including the brown planthopper (BPH), undergo windborne migration that is challenging to observe and track. It remains controversial about their migration patterns and largely unknown regarding the underlying genetic basis. By analyzing 360 whole genomes from around the globe, we clarify the genetic sources of worldwide BPHs and illuminate a landscape of BPH migration showing that East Asian populations perform closed-circuit journeys between Indochina and the Far East, while populations of Malay Archipelago and South Asia undergo one-way migration to Indochina. We further find round-trip migration accelerates population differentiation, with highly diverged regions enriching in a gene desert chromosome that is simultaneously the speciation hotspot between BPH and related species. This study not only shows the power of applying genomic approaches to demystify the migration in windborne migrants but also enhances our understanding of how seasonal movements affect speciation and evolution in insects.

The Imperial College Storm Model (IRIS) Dataset.

Assessing tropical cyclone risk on a global scale given the infrequency of landfalling tropical cyclones (TC) and the short period of reliable observations remains a challenge. Synthetic tropical cyclone datasets can help overcome these problems. Here we present a new global dataset created by IRIS, the ImpeRIal college Storm model. IRIS is novel because, unlike other synthetic TC models, it only simulates the decay from the point of lifetime maximum intensity. This minimises the bias in the dataset. It takes input from 42 years of observed tropical cyclones and creates a 10,000 year synthetic dataset of wind speed which is then validated against the observations. IRIS captures important statistical characteristics of the observed data. The return periods of the landfall maximum wind speed are realistic globally.

Applicability of the generalized wind profile model over mountainous forests.

The analytical equation based on Monin-Obukhov (M-O) similarity theory (i.e., wind profile equation) has been adopted since 1970s for using in the prediction of wind vertical profile over flat terrains, which is mature and accurate. However, its applicability over complex terrains remains unknown. This applicability signifies the accuracy of the estimations of aerodynamic parameters for the boundary layer of non-flat terrain, such as zero-displacement height (d) and aerodynamic roughness length (z0), which will determine the accuracy of frequency correction and source area analysis in calculating carbon, water, and trace gas fluxes based on vorticity covariance method. Therefore, the validation of wind profile model in non-flat terrain is the first step to test whether the flux model needs improvement. We measured three-dimensional wind speed data by using the Ker Towers (three towers in a watershed) at Qingyuan Forest CERN in the Mountainous Region of east Liaoning Province, and compared them with data from Panjin Agricultural Station in the Liaohe Plain, to evaluate the applicability of a generalized wind profile model based on the Monin-Obukhov similarity theory on non-flat terrain. The results showed that the generalized wind profile model could not predict wind speeds accurately of three flux towers separately located in different sites, indicating that wind profile model was not suitable for predicting wind speeds in complex terrains. In the leaf-off and leaf-on periods, the coefficient of determination (R2) between observed and predicted wind speeds ranged from 0.12 to 0.30. Compared to measured values, the standard error of the predicted wind speeds was high up to 2 m·s-1. The predicted wind speeds were high as twice as field-measured wind speed, indicating substantial overestimation. Nevertheless, this model correctly predicted wind speeds in flat agricultural landscape in Panjin Agricultural Station. The R2 between observed wind speeds and predicted wind speed ranged from 0.90 to 0.93. The standard error between observed and predicted values was only 0.5 m·s-1. Results of the F-test showed that the root-mean-square error of the observed and predicted wind speeds in each secondary forest complex terrain was much greater than that in flat agricultural landscape. Terrain was the primary factor affecting the applicability of wind profile model, followed by seasonality (leaf or leafless canopy). The wind profile model was not applicable to the boundary-layer flows over forest canopies in complex terrains, because the d was underestimated or both the d and z0 were underestimated, resulting in inaccurate estimation of aerodynamic height.

Enhancing the control of doubly fed induction generators using artificial neural networks in the presence of real wind profiles.

This study tackles the complex task of integrating wind energy systems into the electric grid, facing challenges such as power oscillations and unreliable energy generation due to fluctuating wind speeds. Focused on wind energy conversion systems, particularly those utilizing double-fed induction generators (DFIGs), the research introduces a novel approach to enhance Direct Power Control (DPC) effectiveness. Traditional DPC, while simple, encounters issues like torque ripples and reduced power quality due to a hysteresis controller. In response, the study proposes an innovative DPC method for DFIGs using artificial neural networks (ANNs). Experimental verification shows ANNs effectively addressing issues with the hysteresis controller and switching table. Additionally, the study addresses wind speed variability by employing an artificial neural network to directly control reactive and active power of DFIG, aiming to minimize challenges with varying wind speeds. Results highlight the effectiveness and reliability of the developed intelligent strategy, outperforming traditional methods by reducing current harmonics and improving dynamic response. This research contributes valuable insights into enhancing the performance and reliability of renewable energy systems, advancing solutions for wind energy integration complexities.

Hurricane season hindsight 2020: Applying the IDEA model toward local tropical cyclone forecasts.

Hurricane Laura began as a disorganized tropical depression in August 2020. Early forecast guidance showed that the tropical cyclone could either completely dissipate or strengthen to a major hurricane as it approached the United States Gulf Coast. While this uncertainty was known by meteorologists, it was not necessarily communicated to the public in a direct manner. As it turned out, the worst-case scenario was the correct one. The tropical depression rapidly intensified and made landfall near Cameron, Louisiana, with sustained winds of 150 mph, making Laura a Category 4 hurricane on the Saffir-Simpson scale. Laura's rapid intensification caught some people off guard. Ideally, weather forecasts would have begun warning Louisiana residents to prepare for the possibility of a devastating hurricane in the early stages of tropical cyclone development. No one is suggesting that meteorologists did anything wrong. However, with the benefit of hindsight and decades of scholarly research in risk communication, we can speculate how an ideal forecast would have been written. This paper demonstrates that there are some simple considerations that could be made that might better alert the public to future hurricane worst-case scenarios, even in uncertain situations.

Song, wind, and mayhem: The Indiana State Fair stage collapse.

The study of planning and execution failures resulting in disastrous outcomes for public events often offers much value when preparing for similar future events. While not recent, the lessons learned from the Indiana State Fair stage collapse of 2011 remain especially pertinent, due to thorough technical and managerial forensic investigations and their rigorous examination during subsequent litigation about the fatal event. Continued concern about life safety and inconsistent building code enforcement and design guidance for publicly occupied temporary structures, eg, outdoor stages, recently drew recommended changes by the International Code Council for the 2024 edition of the International Building Code. Codification of technical lessons learned is seldom immediate. Even with checklists and written plans of action, the full context of managerial lessons learned can be forgotten, as people without first-hand experience of earlier disasters plan later events. Salient events of the past can reinforce valuable lessons for today's practitioners, even to produce building code changes. That is certainly so for the Indiana State Fair stage collapse of August 2011.

[Shelterbelt construction in the scenario of coordinated development of mountain, river, forest, farmland, lake, grassland, and sandy land ecosystems in semi-arid wind-sand areas: Principles and methods]. / 半干旱风沙区"山水林田湖草沙"建设中防护林营建的原理与方法.

We established the systematic concept framework of shelterbelt construction, with "shelterbelts" as the core concern in the construction of integrated ecosystems including mountain, river, forest, farmland, lake, grassland and sandy-land in semi-arid wind-sand areas. In the construction of shelterbelts, it is necessary to adhere to the principles of scientific coordination and systematic management, considering the carrying capacity of water resources, the demand for dust control, the greening and beautification effects, as well as the principle of improving economic benefits. In practice, the construction methods should base on the types and temporal-spatial distribution of shelterbelts, following the shelterbelts construction theory and technology to form different structure and service functions, achieving the functional goals of shelterbelts. By focusing on the key elements including people, forests, grass, fields, water, and sand, we put forward the timeliness, practicality, and scientificity of shelterbelt construction, proposing construction methods for farmland shelterbelts, pastureland shelterbelts, windbreak and sand-fixing forests and protective forest around village (city), which might provide production technical support for the high-quality construction of green ecological barrier in northern China.

[Spatial and temporal distribution characteristics of extreme wind and its effect on wind erosion in Northeast China]. / ä¸œåŒ—åœ°åŒºæžç«¯å¤§é£Žæ—¶ç©ºåˆ†å¸ƒç‰¹å¾åŠå ¶å¯¹é£Žèš€é‡çš„å½±å“.

Under the background of climate change, extreme wind events occur frequently in Northeast China, and the soil erosion caused by these extreme wind events has attracted progressively more attention from scholars. We used the methods of linear analysis, Sen+Mann-Kendal trend analysis, and Kriging interpolation to analyze the spatial and temporal characteristics of extreme wind in Northeast China from 2005 to 2020, and used the RWEQ wind erosion estimation model to calculate the annual soil wind erosion of typical wind erosion sites and wind erosion under extreme wind conditions. The results showed that the extreme wind frequency in Northeast China presented a significant upward trend from 2005 to 2020, with an increase of 2.9 times·a-1. The annual average extreme wind frequency in Northeast China ranged from 1 to 49 times·a-1, and the high frequency areas were distributed in the northwest of Xilin Gol, the west of the Hulunbuir Plateau, and the northeast of Changbai Mountain. The average contribution rate of extreme wind to soil wind erosion in four typical sites (Xilinhot, New Barhu Right Banner, Nenjiang, and Tongyu) was 31%.

Evaluation and optimization of outdoor wind environment in block based on space syntax and CFD simulation.

The wind environment quality at the height of pedestrians can significantly affect the thermal comfort and physical and mental health of pedestrians, promote the diffusion of air pollutants and inhibit the formation of urban heat island effect, and has been paid more and more attention in the field of urban and rural planning. This paper takes Jianlan Road commercial pedestrian Street as an example to maximize the thermal comfort of pedestrians. Based on CFD numerical simulation technology and space syntax theory, the pedestrian wind environment of the accessible space of the block is selected for quantitative research. Through numerical simulation, the influence of block spatial form on the wind environment at pedestrian height under the initial condition of uniform air flow is analyzed, and some suggestions are put forward for the optimization of block spatial form. Finally, the block optimization scheme is verified and simulated. The visualization results show that the wind environment quality of the optimized high-accessibility space is significantly improved, the proportion of comfort zone is increased from 58.2% to 86%, and the static wind rate is reduced from 41.8% to 14%. The wind environment optimization effect is obvious.

Facilitating interaction between partial differential equation-based dynamics and unknown dynamics for regional wind speed prediction.

Regional wind speed prediction is an important spatiotemporal prediction problem which is crucial for optimizing wind power utilization. Nevertheless, the complex dynamics of wind speed pose a formidable challenge to prediction tasks. The evolving dynamics of wind could be governed by underlying physical principles that can be described by partial differential equations (PDE). This study proposes a novel approach called PDE-assisted network (PaNet) for regional wind speed prediction. In PaNet, a new architecture is devised, incorporating both PDE-based dynamics (PDE dynamics) and unknown dynamics. Specifically, this architecture establishes interactions between the two dynamics, regulated by an inter-dynamics communication unit that controls interactions through attention gates. Additionally, recognizing the significance of the initial state for PDE dynamics, an adaptive frequency-gated unit is introduced to generate a suitable initial state for the PDE dynamics by selecting essential frequency components. To evaluate the predictive performance of PaNet, this study conducts comprehensive experiments on two real-world wind speed datasets. The experimental results indicated that the proposed method is superior to other baseline methods.

Operationalizing a fisheries social-ecological system through a Bayesian belief network reveals hotspots for its adaptive capacity in the southern North sea.

Fisheries social-ecological systems (SES) in the North Sea region confront multifaceted challenges stemming from environmental changes, offshore wind farm expansion, and marine protected area establishment. In this paper, we demonstrate the utility of a Bayesian Belief Network (BN) approach in comprehensively capturing and assessing the intricate spatial dynamics within the German plaice-related fisheries SES. The BN integrates ecological, economic, and socio-cultural factors to generate high-resolution maps of profitability and adaptive capacity potential (ACP) as prospective management targets. Our analysis of future scenarios, delineating changes in spatial constraints, economics, and socio-cultural aspects, identifies factors that will exert significant influence on this fisheries SES in the near future. These include the loss of fishing grounds due to the installation of offshore wind farms and marine protected areas, as well as reduced plaice landings due to climate change. The identified ACP hotspots hold the potential to guide the development of localized management strategies and sustainable planning efforts by highlighting the consequences of management decisions. Our findings emphasize the need to consider detailed spatial dynamics of fisheries SES within marine spatial planning (MSP) and illustrate how this information may assist decision-makers and practitioners in area prioritization. We, therefore, propose adopting the concept of fisheries SES within broader integrated management approaches to foster sustainable development of inherently dynamic SES in a rapidly evolving marine environment.

Identification of particle distribution pattern in vertical profile via unmanned aerial vehicles observation.

Below the boundary layer, the air pollutants have been confirmed to present the decreasing trend with the height in most situaitons. However, the disperiosn rate of air pollutants in the vertical profile is rarely investigated in detail, especially through in-situ measurement. With this consideration, we employed an unmanned aerial vehicle equipped with portable monitoring equipments to scrutinize the vertical distribution of PM2.5. Based on the original data, we found that PM2.5 concentration decreases gradually with altitude below the boundary layer and demonstrated an obvious linear correlation. Therefore, the vertical distribution of PM2.5 was quantified by representing the distribution of PM2.5 with the slope of PM2.5 vertical distribution. We used backward trajectories to reveal the causes of outliers (PM2.5 increasing with altitude), and found that PM2.5 in the high altitude came from the southwest. Besides, the relationship between the vertical distribution of PM2.5 and various meteorological factors was investigated using stepwise regression analysis. The results show that the four meteorological factors most strongly correlated with the slope values are: (a) the difference in relative humidity between the ground and the air; (b) the difference in temperature between the ground and the air; (c) the height of the boundary layer; and (d) the wind speed. The slope values increase with increasing the difference in relative humidity between ground and air and the difference in temperature between the ground and the air, and decrease with increasing boundary layer height and wind speed. According to the Random Forest calculations, the ground-to-air relative humidity difference is the most important at 0.718; the wind speed is the least important at 0.053; and the ground-to-air temperature difference and boundary layer height are 0.140 and 0.088, respectively.

Effective-diode-based analysis of industrial solar photovoltaic panel by utilizing novel three-diode solar cell model against conventional single and double solar cell.

Currently, the majority of the country has moved to renewable energy sources for electricity generation, and power companies are concentrating their efforts on renewable resources. Solar, wind, hydropower, and biomass are examples of renewable resources; of these, due to a lack of non-renewable resources, the solar industry is expanding. All year long, solar electricity is available, and it creates a calm, quiet atmosphere. The majority of large and small companies, as well as individual consumers, have shifted to PV solar cells for electricity generation. A trustworthy and precise simulation design of a photovoltaic system prior to installation is required to predict a photovoltaic system's performance. The current research aims to build models for solar PV systems with one, two, and three diodes and determine which model is most appropriate for each environmental circumstance to forecast performance accurately. By contrasting the experimental data of solar panel with simulated results of single-, double-, and triple-diode models, this study examines the accuracy of each model. These models' comparative performance study has been done using the MATLAB/Simulink, taking into account the influence of changing model parameters and the performance of the models under varying climatic circumstances. These models, despite their simplicity, are quite sensitive and react to even a little change in temperature and irradiance. Under conditions of low solar irradiance or shading conditions, three-diode photovoltaic models are shown to be more accurate. We can forecast the power output of solar photovoltaic systems under changeable input circumstances by understanding the I-V curves with the help of the performance assessment of the models used in this work.